load("vcomball20210601.Rda")
d <- vcomball
#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  # NEED REGISTRY NAMES!!
 
  
  #replace number with names
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  
  
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  
  #cro(new.d$siteid) # this is pretty but doesn't show NAs
  #summary(new.d$siteid)
  
  #Using kable function to form a nice table
  
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
Overview of all Registries
Registry Total
Greater CA.10 315
Georgia.20 1754
Northern CA.30 210
Louisiana.40 585
Detroit.60 356
Michigan.61 16
Los Angeles County.80 321

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 6"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## [1] 101079  100849  300631  200312  201605  211392 
## 3551 Levels: 100037  100050  100059  100061  100064  100072  100073  ... 991774
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  
  #To get the number of each registry
  locationname_count<-count(new.d$locationname)
  colnames(locationname_count)<- c("Location", "Total")
  #To get freq of each registry
  locationname_freq1<-table(new.d$locationname)
  locationname_freq<-as.data.frame(round(prop.table(locationname_freq1),3))
  colnames(locationname_freq)<- c("Location", "Freq")
  #Merge them by "Location"
  result<-merge(locationname_count,locationname_freq,by="Location", sort=F)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
Location Total Freq
Detroit 292 0.082
Georgia 1839 0.517
Greater Bay 210 0.059
Greater California 315 0.089
Los Angeles 321 0.090
Louisiana 500 0.141
Virtual 80 0.022

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 14"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
##  [1] 61100276 10100813 10100628 30100177 30100278 30100172 20100653 40102145
##  [9] 10101130 61100327 40100589 20100647 20100647 20103912
## 3543 Levels: 10100003 10100012 10100023 10100024 10100027 10100029 ... 80101425
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 0

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete

All data

st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 2976 83.7 83.7
Online 581 16.3 16.3
Total 3557 100.0 100.0

LA County

n % val%
Paper 321 100 100
Online 0 0 0
Total 321 100 100

Northern CA

n % val%
Paper 168 80 80
Online 42 20 20
Total 210 100 100

Greater CA

n % val%
Paper 275 87.3 87.3
Online 40 12.7 12.7
Total 315 100.0 100.0

Detroit

n % val%
Paper 281 78.9 78.9
Online 75 21.1 21.1
Total 356 100.0 100.0

Louisiana

n % val%
Paper 553 94.5 94.5
Online 32 5.5 5.5
Total 585 100.0 100.0

Georgia

n % val%
Paper 1366 77.9 77.9
Online 388 22.1 22.1
Total 1754 100.0 100.0

Michigan

n % val%
Paper 12 75 75
Online 4 25 25
Total 16 100 100

A1

  • A1. In what month and year were you first diagnosed with prostate cancer?

All data

# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
0 1 0.0 0.0
0* 1 0.0 0.0
1 227 6.4 6.4
10 268 7.5 7.5
11 215 6.0 6.0
12 168 4.7 4.7
18 1 0.0 0.0
2 227 6.4 6.4
22 1 0.0 0.0
25 2 0.1 0.1
3 278 7.8 7.8
4 284 8.0 8.0
48 1 0.0 0.0
5 264 7.4 7.4
6 426 12.0 12.0
7 258 7.3 7.3
8 236 6.6 6.6
9 221 6.2 6.2
“NA” 478 13.4 13.4
Total 3557 100.0 100.0
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
1914 1 0.0 0.0
1915 3 0.1 0.1
1916 7 0.2 0.2
1917 9 0.3 0.3
1918 3 0.1 0.1
1937 1 0.0 0.0
1941 1 0.0 0.0
1943 1 0.0 0.0
1944 1 0.0 0.0
1945 2 0.1 0.1
1946 1 0.0 0.0
1947 2 0.1 0.1
1948 2 0.1 0.1
1949 2 0.1 0.1
1950 3 0.1 0.1
1951 2 0.1 0.1
1952 3 0.1 0.1
1953 1 0.0 0.0
1954 2 0.1 0.1
1955 2 0.1 0.1
1956 1 0.0 0.0
1963 2 0.1 0.1
1965 1 0.0 0.0
1987 1 0.0 0.0
1989 1 0.0 0.0
1990 1 0.0 0.0
1993 3 0.1 0.1
1994 1 0.0 0.0
1995 2 0.1 0.1
1996 1 0.0 0.0
1997 1 0.0 0.0
1998 4 0.1 0.1
1999 4 0.1 0.1
20 1 0.0 0.0
2000 1 0.0 0.0
2001 1 0.0 0.0
2002 1 0.0 0.0
2003 2 0.1 0.1
2004 6 0.2 0.2
2005 6 0.2 0.2
2006 5 0.1 0.1
2007 4 0.1 0.1
2008 8 0.2 0.2
2009 7 0.2 0.2
2010 17 0.5 0.5
2011 13 0.4 0.4
2012 29 0.8 0.8
2013 63 1.8 1.8
2014 177 5.0 5.0
2015 860 24.2 24.2
2016 1157 32.5 32.5
2017 551 15.5 15.5
2018 200 5.6 5.6
2019 52 1.5 1.5
2020 22 0.6 0.6
2021 5 0.1 0.1
615 1 0.0 0.0
“NA” 296 8.3 8.3
Total 3557 100.0 100.0
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed") 
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 10 0.3 0.3
HAVE_had_ProstateCancer 3547 99.7 99.7
Total 3557 100.0 100.0

LA County

A1:month diagnosed
n % val%
0 1 0.3 0.3
0* 0 0.0 0.0
1 25 7.8 7.8
10 22 6.9 6.9
11 13 4.0 4.0
12 12 3.7 3.7
18 0 0.0 0.0
2 13 4.0 4.0
22 0 0.0 0.0
25 0 0.0 0.0
3 20 6.2 6.2
4 21 6.5 6.5
48 0 0.0 0.0
5 25 7.8 7.8
6 22 6.9 6.9
7 15 4.7 4.7
8 18 5.6 5.6
9 13 4.0 4.0
“NA” 101 31.5 31.5
Total 321 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 0 0.0 0.0
1916 0 0.0 0.0
1917 0 0.0 0.0
1918 0 0.0 0.0
1937 0 0.0 0.0
1941 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 0 0.0 0.0
1946 0 0.0 0.0
1947 0 0.0 0.0
1948 0 0.0 0.0
1949 0 0.0 0.0
1950 0 0.0 0.0
1951 0 0.0 0.0
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 0 0.0 0.0
1956 0 0.0 0.0
1963 0 0.0 0.0
1965 1 0.3 0.3
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 0 0.0 0.0
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 0 0.0 0.0
1999 0 0.0 0.0
20 0 0.0 0.0
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 0 0.0 0.0
2003 0 0.0 0.0
2004 0 0.0 0.0
2005 0 0.0 0.0
2006 1 0.3 0.3
2007 0 0.0 0.0
2008 2 0.6 0.6
2009 1 0.3 0.3
2010 1 0.3 0.3
2011 0 0.0 0.0
2012 0 0.0 0.0
2013 5 1.6 1.6
2014 8 2.5 2.5
2015 85 26.5 26.5
2016 122 38.0 38.0
2017 64 19.9 19.9
2018 19 5.9 5.9
2019 0 0.0 0.0
2020 1 0.3 0.3
2021 0 0.0 0.0
615 0 0.0 0.0
“NA” 11 3.4 3.4
Total 321 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 321 100 100
Total 321 100 100

Northern CA

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 0 0.0 0.0
1 16 7.6 7.6
10 21 10.0 10.0
11 13 6.2 6.2
12 5 2.4 2.4
18 0 0.0 0.0
2 18 8.6 8.6
22 0 0.0 0.0
25 0 0.0 0.0
3 21 10.0 10.0
4 11 5.2 5.2
48 0 0.0 0.0
5 14 6.7 6.7
6 22 10.5 10.5
7 13 6.2 6.2
8 15 7.1 7.1
9 11 5.2 5.2
“NA” 30 14.3 14.3
Total 210 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 0 0.0 0.0
1916 1 0.5 0.5
1917 0 0.0 0.0
1918 0 0.0 0.0
1937 0 0.0 0.0
1941 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 0 0.0 0.0
1946 1 0.5 0.5
1947 0 0.0 0.0
1948 0 0.0 0.0
1949 0 0.0 0.0
1950 1 0.5 0.5
1951 0 0.0 0.0
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 0 0.0 0.0
1956 0 0.0 0.0
1963 0 0.0 0.0
1965 0 0.0 0.0
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 0 0.0 0.0
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 0 0.0 0.0
1999 0 0.0 0.0
20 0 0.0 0.0
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 0 0.0 0.0
2003 0 0.0 0.0
2004 0 0.0 0.0
2005 0 0.0 0.0
2006 1 0.5 0.5
2007 0 0.0 0.0
2008 0 0.0 0.0
2009 0 0.0 0.0
2010 0 0.0 0.0
2011 1 0.5 0.5
2012 3 1.4 1.4
2013 2 1.0 1.0
2014 8 3.8 3.8
2015 62 29.5 29.5
2016 62 29.5 29.5
2017 35 16.7 16.7
2018 6 2.9 2.9
2019 2 1.0 1.0
2020 1 0.5 0.5
2021 0 0.0 0.0
615 0 0.0 0.0
“NA” 24 11.4 11.4
Total 210 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 210 100 100
Total 210 100 100

Greater CA

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 0 0.0 0.0
1 18 5.7 5.7
10 27 8.6 8.6
11 21 6.7 6.7
12 17 5.4 5.4
18 0 0.0 0.0
2 19 6.0 6.0
22 0 0.0 0.0
25 0 0.0 0.0
3 20 6.3 6.3
4 26 8.3 8.3
48 0 0.0 0.0
5 23 7.3 7.3
6 46 14.6 14.6
7 24 7.6 7.6
8 22 7.0 7.0
9 19 6.0 6.0
“NA” 33 10.5 10.5
Total 315 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 1 0.3 0.3
1916 0 0.0 0.0
1917 1 0.3 0.3
1918 1 0.3 0.3
1937 0 0.0 0.0
1941 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 1 0.3 0.3
1946 0 0.0 0.0
1947 0 0.0 0.0
1948 0 0.0 0.0
1949 0 0.0 0.0
1950 1 0.3 0.3
1951 1 0.3 0.3
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 0 0.0 0.0
1956 0 0.0 0.0
1963 0 0.0 0.0
1965 0 0.0 0.0
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 1 0.3 0.3
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 0 0.0 0.0
1999 0 0.0 0.0
20 0 0.0 0.0
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 1 0.3 0.3
2003 0 0.0 0.0
2004 1 0.3 0.3
2005 2 0.6 0.6
2006 0 0.0 0.0
2007 0 0.0 0.0
2008 0 0.0 0.0
2009 1 0.3 0.3
2010 4 1.3 1.3
2011 0 0.0 0.0
2012 1 0.3 0.3
2013 4 1.3 1.3
2014 18 5.7 5.7
2015 91 28.9 28.9
2016 123 39.0 39.0
2017 26 8.3 8.3
2018 7 2.2 2.2
2019 3 1.0 1.0
2020 1 0.3 0.3
2021 0 0.0 0.0
615 0 0.0 0.0
“NA” 25 7.9 7.9
Total 315 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 315 100 100
Total 315 100 100

Detroit

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 0 0.0 0.0
1 26 7.3 7.3
10 21 5.9 5.9
11 16 4.5 4.5
12 19 5.3 5.3
18 1 0.3 0.3
2 18 5.1 5.1
22 0 0.0 0.0
25 0 0.0 0.0
3 33 9.3 9.3
4 27 7.6 7.6
48 0 0.0 0.0
5 19 5.3 5.3
6 50 14.0 14.0
7 28 7.9 7.9
8 25 7.0 7.0
9 26 7.3 7.3
“NA” 47 13.2 13.2
Total 356 100.0 100.0
A1:year diagnosed
n % val%
1914 1 0.3 0.3
1915 1 0.3 0.3
1916 1 0.3 0.3
1917 1 0.3 0.3
1918 1 0.3 0.3
1937 1 0.3 0.3
1941 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 0 0.0 0.0
1946 0 0.0 0.0
1947 1 0.3 0.3
1948 1 0.3 0.3
1949 0 0.0 0.0
1950 1 0.3 0.3
1951 0 0.0 0.0
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 1 0.3 0.3
1956 0 0.0 0.0
1963 2 0.6 0.6
1965 0 0.0 0.0
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 0 0.0 0.0
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 1 0.3 0.3
1999 1 0.3 0.3
20 1 0.3 0.3
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 0 0.0 0.0
2003 1 0.3 0.3
2004 1 0.3 0.3
2005 1 0.3 0.3
2006 0 0.0 0.0
2007 1 0.3 0.3
2008 0 0.0 0.0
2009 0 0.0 0.0
2010 2 0.6 0.6
2011 3 0.8 0.8
2012 4 1.1 1.1
2013 17 4.8 4.8
2014 26 7.3 7.3
2015 51 14.3 14.3
2016 66 18.5 18.5
2017 71 19.9 19.9
2018 48 13.5 13.5
2019 11 3.1 3.1
2020 1 0.3 0.3
2021 2 0.6 0.6
615 1 0.3 0.3
“NA” 34 9.6 9.6
Total 356 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 1 0.3 0.3
HAVE_had_ProstateCancer 355 99.7 99.7
Total 356 100.0 100.0

Louisiana

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 0 0.0 0.0
1 37 6.3 6.3
10 43 7.4 7.4
11 32 5.5 5.5
12 26 4.4 4.4
18 0 0.0 0.0
2 45 7.7 7.7
22 1 0.2 0.2
25 1 0.2 0.2
3 60 10.3 10.3
4 62 10.6 10.6
48 0 0.0 0.0
5 54 9.2 9.2
6 80 13.7 13.7
7 48 8.2 8.2
8 29 5.0 5.0
9 40 6.8 6.8
“NA” 27 4.6 4.6
Total 585 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 1 0.2 0.2
1916 0 0.0 0.0
1917 3 0.5 0.5
1918 0 0.0 0.0
1937 0 0.0 0.0
1941 0 0.0 0.0
1943 1 0.2 0.2
1944 0 0.0 0.0
1945 0 0.0 0.0
1946 0 0.0 0.0
1947 0 0.0 0.0
1948 0 0.0 0.0
1949 0 0.0 0.0
1950 0 0.0 0.0
1951 0 0.0 0.0
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 0 0.0 0.0
1956 0 0.0 0.0
1963 0 0.0 0.0
1965 0 0.0 0.0
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 0 0.0 0.0
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 1 0.2 0.2
1999 0 0.0 0.0
20 0 0.0 0.0
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 0 0.0 0.0
2003 0 0.0 0.0
2004 1 0.2 0.2
2005 0 0.0 0.0
2006 0 0.0 0.0
2007 0 0.0 0.0
2008 2 0.3 0.3
2009 0 0.0 0.0
2010 2 0.3 0.3
2011 2 0.3 0.3
2012 2 0.3 0.3
2013 5 0.9 0.9
2014 22 3.8 3.8
2015 98 16.8 16.8
2016 234 40.0 40.0
2017 141 24.1 24.1
2018 37 6.3 6.3
2019 13 2.2 2.2
2020 2 0.3 0.3
2021 0 0.0 0.0
615 0 0.0 0.0
“NA” 18 3.1 3.1
Total 585 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 5 0.9 0.9
HAVE_had_ProstateCancer 580 99.1 99.1
Total 585 100.0 100.0

Georgia

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 1 0.1 0.1
1 102 5.8 5.8
10 133 7.6 7.6
11 120 6.8 6.8
12 87 5.0 5.0
18 0 0.0 0.0
2 112 6.4 6.4
22 0 0.0 0.0
25 1 0.1 0.1
3 121 6.9 6.9
4 136 7.8 7.8
48 1 0.1 0.1
5 129 7.4 7.4
6 204 11.6 11.6
7 130 7.4 7.4
8 125 7.1 7.1
9 112 6.4 6.4
“NA” 240 13.7 13.7
Total 1754 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 0 0.0 0.0
1916 5 0.3 0.3
1917 4 0.2 0.2
1918 1 0.1 0.1
1937 0 0.0 0.0
1941 1 0.1 0.1
1943 0 0.0 0.0
1944 1 0.1 0.1
1945 1 0.1 0.1
1946 0 0.0 0.0
1947 1 0.1 0.1
1948 1 0.1 0.1
1949 2 0.1 0.1
1950 0 0.0 0.0
1951 1 0.1 0.1
1952 3 0.2 0.2
1953 1 0.1 0.1
1954 2 0.1 0.1
1955 1 0.1 0.1
1956 1 0.1 0.1
1963 0 0.0 0.0
1965 0 0.0 0.0
1987 1 0.1 0.1
1989 1 0.1 0.1
1990 1 0.1 0.1
1993 3 0.2 0.2
1994 0 0.0 0.0
1995 2 0.1 0.1
1996 1 0.1 0.1
1997 1 0.1 0.1
1998 2 0.1 0.1
1999 3 0.2 0.2
20 0 0.0 0.0
2000 1 0.1 0.1
2001 1 0.1 0.1
2002 0 0.0 0.0
2003 1 0.1 0.1
2004 3 0.2 0.2
2005 3 0.2 0.2
2006 3 0.2 0.2
2007 3 0.2 0.2
2008 4 0.2 0.2
2009 5 0.3 0.3
2010 7 0.4 0.4
2011 6 0.3 0.3
2012 17 1.0 1.0
2013 25 1.4 1.4
2014 90 5.1 5.1
2015 472 26.9 26.9
2016 550 31.4 31.4
2017 214 12.2 12.2
2018 83 4.7 4.7
2019 22 1.3 1.3
2020 16 0.9 0.9
2021 3 0.2 0.2
615 0 0.0 0.0
“NA” 184 10.5 10.5
Total 1754 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 4 0.2 0.2
HAVE_had_ProstateCancer 1750 99.8 99.8
Total 1754 100.0 100.0

Michigan

A1:month diagnosed
n % val%
0 0 0.0 0.0
0* 0 0.0 0.0
1 3 18.8 18.8
10 1 6.2 6.2
11 0 0.0 0.0
12 2 12.5 12.5
18 0 0.0 0.0
2 2 12.5 12.5
22 0 0.0 0.0
25 0 0.0 0.0
3 3 18.8 18.8
4 1 6.2 6.2
48 0 0.0 0.0
5 0 0.0 0.0
6 2 12.5 12.5
7 0 0.0 0.0
8 2 12.5 12.5
9 0 0.0 0.0
“NA” 0 0.0 0.0
Total 16 100.0 100.0
A1:year diagnosed
n % val%
1914 0 0.0 0.0
1915 0 0.0 0.0
1916 0 0.0 0.0
1917 0 0.0 0.0
1918 0 0.0 0.0
1937 0 0.0 0.0
1941 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 0 0.0 0.0
1946 0 0.0 0.0
1947 0 0.0 0.0
1948 0 0.0 0.0
1949 0 0.0 0.0
1950 0 0.0 0.0
1951 0 0.0 0.0
1952 0 0.0 0.0
1953 0 0.0 0.0
1954 0 0.0 0.0
1955 0 0.0 0.0
1956 0 0.0 0.0
1963 0 0.0 0.0
1965 0 0.0 0.0
1987 0 0.0 0.0
1989 0 0.0 0.0
1990 0 0.0 0.0
1993 0 0.0 0.0
1994 0 0.0 0.0
1995 0 0.0 0.0
1996 0 0.0 0.0
1997 0 0.0 0.0
1998 0 0.0 0.0
1999 0 0.0 0.0
20 0 0.0 0.0
2000 0 0.0 0.0
2001 0 0.0 0.0
2002 0 0.0 0.0
2003 0 0.0 0.0
2004 0 0.0 0.0
2005 0 0.0 0.0
2006 0 0.0 0.0
2007 0 0.0 0.0
2008 0 0.0 0.0
2009 0 0.0 0.0
2010 1 6.2 6.2
2011 1 6.2 6.2
2012 2 12.5 12.5
2013 5 31.2 31.2
2014 5 31.2 31.2
2015 1 6.2 6.2
2016 0 0.0 0.0
2017 0 0.0 0.0
2018 0 0.0 0.0
2019 1 6.2 6.2
2020 0 0.0 0.0
2021 0 0.0 0.0
615 0 0.0 0.0
“NA” 0 0.0 0.0
Total 16 100.0 100.0
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 16 100 100
Total 16 100 100

A2

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No

All data

a2 <- as.factor(d[,"a2"])
levels(a2) <- list(No="1",
                   Yes="2",
                   Scantron_Error="*")
  a2 <- ordered(a2, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 3257 91.6 99.7
No 10 0.3 0.3
Scantron_Error 0 0.0 0.0
NA 290 8.2 NA
Total 3557 100.0 100.0

LA County

A2
n % val%
Yes 303 94.4 100
No 0 0.0 0
Scantron_Error 0 0.0 0
NA 18 5.6 NA
Total 321 100.0 100

Northern CA

A2
n % val%
Yes 195 92.9 100
No 0 0.0 0
Scantron_Error 0 0.0 0
NA 15 7.1 NA
Total 210 100.0 100

Greater CA

A2
n % val%
Yes 289 91.7 99.3
No 2 0.6 0.7
Scantron_Error 0 0.0 0.0
NA 24 7.6 NA
Total 315 100.0 100.0

Detroit

A2
n % val%
Yes 343 96.3 99.4
No 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 11 3.1 NA
Total 356 100.0 100.0

Louisiana

A2
n % val%
Yes 526 89.9 99.2
No 4 0.7 0.8
Scantron_Error 0 0.0 0.0
NA 55 9.4 NA
Total 585 100.0 100.0

Georgia

A2
n % val%
Yes 1586 90.4 99.9
No 2 0.1 0.1
Scantron_Error 0 0.0 0.0
NA 166 9.5 NA
Total 1754 100.0 100.0

Michigan

A2
n % val%
Yes 15 93.8 100
No 0 0.0 0
Scantron_Error 0 0.0 0
NA 1 6.2 NA
Total 16 100.0 100

A4

A4. What is your month and year of birth?

All data

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 303 8.5 8.5
10 314 8.8 8.8
11 283 8.0 8.0
12 333 9.4 9.4
18 1 0.0 0.0
2 266 7.5 7.5
22 1 0.0 0.0
24 1 0.0 0.0
25 1 0.0 0.0
26 1 0.0 0.0
3 282 7.9 7.9
31 1 0.0 0.0
33 1 0.0 0.0
35 1 0.0 0.0
4 246 6.9 6.9
48 1 0.0 0.0
5 269 7.6 7.6
57 1 0.0 0.0
58 1 0.0 0.0
6 297 8.3 8.3
61 1 0.0 0.0
7 297 8.3 8.3
71 2 0.1 0.1
8 336 9.4 9.4
9 293 8.2 8.2
96 1 0.0 0.0
“NA” 23 0.6 0.6
Total 3557 100.0 100.0
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1062 1 0.0 0.0
1340 1 0.0 0.0
1930 1 0.0 0.0
1933 1 0.0 0.0
1936 1 0.0 0.0
1937 10 0.3 0.3
1938 10 0.3 0.3
1939 9 0.3 0.3
1940 19 0.5 0.5
1941 59 1.7 1.7
1942 63 1.8 1.8
1943 84 2.4 2.4
1944 85 2.4 2.4
1945 104 2.9 2.9
1946 145 4.1 4.1
1947 140 3.9 3.9
1948 192 5.4 5.4
1949 201 5.7 5.7
1950 208 5.8 5.8
1951 199 5.6 5.6
1952 187 5.3 5.3
1953 175 4.9 4.9
1954 180 5.1 5.1
1955 180 5.1 5.1
1956 191 5.4 5.4
1957 190 5.3 5.3
1958 133 3.7 3.7
1959 107 3.0 3.0
1960 147 4.1 4.1
1961 104 2.9 2.9
1962 79 2.2 2.2
1963 73 2.1 2.1
1964 57 1.6 1.6
1965 45 1.3 1.3
1966 37 1.0 1.0
1967 32 0.9 0.9
1968 24 0.7 0.7
1969 19 0.5 0.5
1970 13 0.4 0.4
1971 8 0.2 0.2
1972 3 0.1 0.1
1973 12 0.3 0.3
1974 1 0.0 0.0
1976 2 0.1 0.1
1977 1 0.0 0.0
1978 1 0.0 0.0
2015 3 0.1 0.1
2018 1 0.0 0.0
748 1 0.0 0.0
“NA” 18 0.5 0.5
Total 3557 100.0 100.0

LA County

A4: Month of birth
n % val%
1 28 8.7 8.7
10 27 8.4 8.4
11 28 8.7 8.7
12 29 9.0 9.0
18 0 0.0 0.0
2 23 7.2 7.2
22 0 0.0 0.0
24 1 0.3 0.3
25 0 0.0 0.0
26 0 0.0 0.0
3 31 9.7 9.7
31 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
4 19 5.9 5.9
48 0 0.0 0.0
5 22 6.9 6.9
57 0 0.0 0.0
58 1 0.3 0.3
6 29 9.0 9.0
61 0 0.0 0.0
7 23 7.2 7.2
71 0 0.0 0.0
8 35 10.9 10.9
9 23 7.2 7.2
96 0 0.0 0.0
“NA” 2 0.6 0.6
Total 321 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 0 0.0 0.0
1936 0 0.0 0.0
1937 2 0.6 0.6
1938 1 0.3 0.3
1939 3 0.9 0.9
1940 0 0.0 0.0
1941 2 0.6 0.6
1942 8 2.5 2.5
1943 8 2.5 2.5
1944 6 1.9 1.9
1945 8 2.5 2.5
1946 14 4.4 4.4
1947 15 4.7 4.7
1948 19 5.9 5.9
1949 15 4.7 4.7
1950 17 5.3 5.3
1951 16 5.0 5.0
1952 21 6.5 6.5
1953 16 5.0 5.0
1954 18 5.6 5.6
1955 18 5.6 5.6
1956 18 5.6 5.6
1957 16 5.0 5.0
1958 10 3.1 3.1
1959 8 2.5 2.5
1960 10 3.1 3.1
1961 13 4.0 4.0
1962 5 1.6 1.6
1963 5 1.6 1.6
1964 3 0.9 0.9
1965 4 1.2 1.2
1966 8 2.5 2.5
1967 5 1.6 1.6
1968 3 0.9 0.9
1969 2 0.6 0.6
1970 0 0.0 0.0
1971 0 0.0 0.0
1972 0 0.0 0.0
1973 1 0.3 0.3
1974 0 0.0 0.0
1976 0 0.0 0.0
1977 0 0.0 0.0
1978 0 0.0 0.0
2015 1 0.3 0.3
2018 1 0.3 0.3
748 0 0.0 0.0
“NA” 1 0.3 0.3
Total 321 100.0 100.0

Northern CA

A4: Month of birth
n % val%
1 14 6.7 6.7
10 20 9.5 9.5
11 23 11.0 11.0
12 27 12.9 12.9
18 0 0.0 0.0
2 11 5.2 5.2
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
3 15 7.1 7.1
31 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
4 16 7.6 7.6
48 0 0.0 0.0
5 16 7.6 7.6
57 0 0.0 0.0
58 0 0.0 0.0
6 20 9.5 9.5
61 0 0.0 0.0
7 13 6.2 6.2
71 0 0.0 0.0
8 16 7.6 7.6
9 18 8.6 8.6
96 0 0.0 0.0
“NA” 1 0.5 0.5
Total 210 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 0 0.0 0.0
1936 0 0.0 0.0
1937 1 0.5 0.5
1938 1 0.5 0.5
1939 0 0.0 0.0
1940 1 0.5 0.5
1941 2 1.0 1.0
1942 8 3.8 3.8
1943 3 1.4 1.4
1944 3 1.4 1.4
1945 6 2.9 2.9
1946 11 5.2 5.2
1947 12 5.7 5.7
1948 8 3.8 3.8
1949 14 6.7 6.7
1950 13 6.2 6.2
1951 18 8.6 8.6
1952 10 4.8 4.8
1953 11 5.2 5.2
1954 13 6.2 6.2
1955 3 1.4 1.4
1956 5 2.4 2.4
1957 10 4.8 4.8
1958 16 7.6 7.6
1959 8 3.8 3.8
1960 9 4.3 4.3
1961 3 1.4 1.4
1962 1 0.5 0.5
1963 3 1.4 1.4
1964 3 1.4 1.4
1965 2 1.0 1.0
1966 2 1.0 1.0
1967 2 1.0 1.0
1968 1 0.5 0.5
1969 4 1.9 1.9
1970 1 0.5 0.5
1971 0 0.0 0.0
1972 0 0.0 0.0
1973 0 0.0 0.0
1974 0 0.0 0.0
1976 1 0.5 0.5
1977 0 0.0 0.0
1978 0 0.0 0.0
2015 0 0.0 0.0
2018 0 0.0 0.0
748 0 0.0 0.0
“NA” 1 0.5 0.5
Total 210 100.0 100.0

Greater CA

A4: Month of birth
n % val%
1 24 7.6 7.6
10 26 8.3 8.3
11 28 8.9 8.9
12 22 7.0 7.0
18 0 0.0 0.0
2 25 7.9 7.9
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
3 18 5.7 5.7
31 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
4 19 6.0 6.0
48 0 0.0 0.0
5 31 9.8 9.8
57 0 0.0 0.0
58 0 0.0 0.0
6 29 9.2 9.2
61 0 0.0 0.0
7 38 12.1 12.1
71 0 0.0 0.0
8 29 9.2 9.2
9 22 7.0 7.0
96 0 0.0 0.0
“NA” 4 1.3 1.3
Total 315 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 0 0.0 0.0
1936 0 0.0 0.0
1937 0 0.0 0.0
1938 0 0.0 0.0
1939 0 0.0 0.0
1940 0 0.0 0.0
1941 6 1.9 1.9
1942 6 1.9 1.9
1943 6 1.9 1.9
1944 4 1.3 1.3
1945 13 4.1 4.1
1946 14 4.4 4.4
1947 11 3.5 3.5
1948 15 4.8 4.8
1949 24 7.6 7.6
1950 17 5.4 5.4
1951 17 5.4 5.4
1952 11 3.5 3.5
1953 15 4.8 4.8
1954 20 6.3 6.3
1955 17 5.4 5.4
1956 13 4.1 4.1
1957 14 4.4 4.4
1958 9 2.9 2.9
1959 9 2.9 2.9
1960 22 7.0 7.0
1961 11 3.5 3.5
1962 8 2.5 2.5
1963 10 3.2 3.2
1964 2 0.6 0.6
1965 8 2.5 2.5
1966 0 0.0 0.0
1967 2 0.6 0.6
1968 1 0.3 0.3
1969 2 0.6 0.6
1970 0 0.0 0.0
1971 1 0.3 0.3
1972 0 0.0 0.0
1973 1 0.3 0.3
1974 0 0.0 0.0
1976 0 0.0 0.0
1977 0 0.0 0.0
1978 1 0.3 0.3
2015 0 0.0 0.0
2018 0 0.0 0.0
748 1 0.3 0.3
“NA” 4 1.3 1.3
Total 315 100.0 100.0

Detroit

A4: Month of birth
n % val%
1 28 7.9 7.9
10 36 10.1 10.1
11 24 6.7 6.7
12 23 6.5 6.5
18 1 0.3 0.3
2 28 7.9 7.9
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
3 24 6.7 6.7
31 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
4 31 8.7 8.7
48 0 0.0 0.0
5 21 5.9 5.9
57 0 0.0 0.0
58 0 0.0 0.0
6 34 9.6 9.6
61 0 0.0 0.0
7 29 8.1 8.1
71 1 0.3 0.3
8 40 11.2 11.2
9 36 10.1 10.1
96 0 0.0 0.0
“NA” 0 0.0 0.0
Total 356 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 1 0.3 0.3
1936 0 0.0 0.0
1937 4 1.1 1.1
1938 1 0.3 0.3
1939 3 0.8 0.8
1940 2 0.6 0.6
1941 8 2.2 2.2
1942 7 2.0 2.0
1943 9 2.5 2.5
1944 8 2.2 2.2
1945 9 2.5 2.5
1946 11 3.1 3.1
1947 18 5.1 5.1
1948 9 2.5 2.5
1949 14 3.9 3.9
1950 18 5.1 5.1
1951 16 4.5 4.5
1952 15 4.2 4.2
1953 11 3.1 3.1
1954 17 4.8 4.8
1955 26 7.3 7.3
1956 26 7.3 7.3
1957 27 7.6 7.6
1958 16 4.5 4.5
1959 8 2.2 2.2
1960 14 3.9 3.9
1961 12 3.4 3.4
1962 14 3.9 3.9
1963 8 2.2 2.2
1964 4 1.1 1.1
1965 3 0.8 0.8
1966 4 1.1 1.1
1967 3 0.8 0.8
1968 1 0.3 0.3
1969 3 0.8 0.8
1970 2 0.6 0.6
1971 1 0.3 0.3
1972 0 0.0 0.0
1973 2 0.6 0.6
1974 0 0.0 0.0
1976 0 0.0 0.0
1977 1 0.3 0.3
1978 0 0.0 0.0
2015 0 0.0 0.0
2018 0 0.0 0.0
748 0 0.0 0.0
“NA” 0 0.0 0.0
Total 356 100.0 100.0

Louisiana

A4: Month of birth
n % val%
1 45 7.7 7.7
10 57 9.7 9.7
11 50 8.5 8.5
12 61 10.4 10.4
18 0 0.0 0.0
2 47 8.0 8.0
22 0 0.0 0.0
24 0 0.0 0.0
25 1 0.2 0.2
26 0 0.0 0.0
3 40 6.8 6.8
31 1 0.2 0.2
33 0 0.0 0.0
35 0 0.0 0.0
4 37 6.3 6.3
48 0 0.0 0.0
5 31 5.3 5.3
57 0 0.0 0.0
58 0 0.0 0.0
6 49 8.4 8.4
61 0 0.0 0.0
7 57 9.7 9.7
71 0 0.0 0.0
8 49 8.4 8.4
9 55 9.4 9.4
96 0 0.0 0.0
“NA” 5 0.9 0.9
Total 585 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 0 0.0 0.0
1936 1 0.2 0.2
1937 3 0.5 0.5
1938 7 1.2 1.2
1939 2 0.3 0.3
1940 9 1.5 1.5
1941 12 2.1 2.1
1942 6 1.0 1.0
1943 13 2.2 2.2
1944 14 2.4 2.4
1945 21 3.6 3.6
1946 16 2.7 2.7
1947 23 3.9 3.9
1948 31 5.3 5.3
1949 33 5.6 5.6
1950 31 5.3 5.3
1951 31 5.3 5.3
1952 28 4.8 4.8
1953 45 7.7 7.7
1954 25 4.3 4.3
1955 29 5.0 5.0
1956 38 6.5 6.5
1957 30 5.1 5.1
1958 19 3.2 3.2
1959 17 2.9 2.9
1960 19 3.2 3.2
1961 20 3.4 3.4
1962 13 2.2 2.2
1963 14 2.4 2.4
1964 9 1.5 1.5
1965 5 0.9 0.9
1966 3 0.5 0.5
1967 7 1.2 1.2
1968 2 0.3 0.3
1969 1 0.2 0.2
1970 2 0.3 0.3
1971 1 0.2 0.2
1972 1 0.2 0.2
1973 0 0.0 0.0
1974 0 0.0 0.0
1976 0 0.0 0.0
1977 0 0.0 0.0
1978 0 0.0 0.0
2015 1 0.2 0.2
2018 0 0.0 0.0
748 0 0.0 0.0
“NA” 3 0.5 0.5
Total 585 100.0 100.0

Georgia

A4: Month of birth
n % val%
1 163 9.3 9.3
10 146 8.3 8.3
11 130 7.4 7.4
12 169 9.6 9.6
18 0 0.0 0.0
2 130 7.4 7.4
22 1 0.1 0.1
24 0 0.0 0.0
25 0 0.0 0.0
26 1 0.1 0.1
3 154 8.8 8.8
31 0 0.0 0.0
33 1 0.1 0.1
35 1 0.1 0.1
4 121 6.9 6.9
48 1 0.1 0.1
5 147 8.4 8.4
57 1 0.1 0.1
58 0 0.0 0.0
6 135 7.7 7.7
61 1 0.1 0.1
7 135 7.7 7.7
71 1 0.1 0.1
8 166 9.5 9.5
9 138 7.9 7.9
96 1 0.1 0.1
“NA” 11 0.6 0.6
Total 1754 100.0 100.0
A4: Year of birth
n % val%
1062 1 0.1 0.1
1340 1 0.1 0.1
1930 1 0.1 0.1
1933 0 0.0 0.0
1936 0 0.0 0.0
1937 0 0.0 0.0
1938 0 0.0 0.0
1939 1 0.1 0.1
1940 7 0.4 0.4
1941 29 1.7 1.7
1942 28 1.6 1.6
1943 45 2.6 2.6
1944 50 2.9 2.9
1945 46 2.6 2.6
1946 79 4.5 4.5
1947 61 3.5 3.5
1948 109 6.2 6.2
1949 99 5.6 5.6
1950 111 6.3 6.3
1951 101 5.8 5.8
1952 101 5.8 5.8
1953 76 4.3 4.3
1954 86 4.9 4.9
1955 86 4.9 4.9
1956 88 5.0 5.0
1957 93 5.3 5.3
1958 63 3.6 3.6
1959 56 3.2 3.2
1960 72 4.1 4.1
1961 45 2.6 2.6
1962 37 2.1 2.1
1963 33 1.9 1.9
1964 36 2.1 2.1
1965 23 1.3 1.3
1966 19 1.1 1.1
1967 13 0.7 0.7
1968 16 0.9 0.9
1969 7 0.4 0.4
1970 8 0.5 0.5
1971 5 0.3 0.3
1972 2 0.1 0.1
1973 8 0.5 0.5
1974 1 0.1 0.1
1976 1 0.1 0.1
1977 0 0.0 0.0
1978 0 0.0 0.0
2015 1 0.1 0.1
2018 0 0.0 0.0
748 0 0.0 0.0
“NA” 9 0.5 0.5
Total 1754 100.0 100.0

Michigan

A4: Month of birth
n % val%
1 1 6.2 6.2
10 2 12.5 12.5
11 0 0.0 0.0
12 2 12.5 12.5
18 0 0.0 0.0
2 2 12.5 12.5
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
3 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
4 3 18.8 18.8
48 0 0.0 0.0
5 1 6.2 6.2
57 0 0.0 0.0
58 0 0.0 0.0
6 1 6.2 6.2
61 0 0.0 0.0
7 2 12.5 12.5
71 0 0.0 0.0
8 1 6.2 6.2
9 1 6.2 6.2
96 0 0.0 0.0
“NA” 0 0.0 0.0
Total 16 100.0 100.0
A4: Year of birth
n % val%
1062 0 0.0 0.0
1340 0 0.0 0.0
1930 0 0.0 0.0
1933 0 0.0 0.0
1936 0 0.0 0.0
1937 0 0.0 0.0
1938 0 0.0 0.0
1939 0 0.0 0.0
1940 0 0.0 0.0
1941 0 0.0 0.0
1942 0 0.0 0.0
1943 0 0.0 0.0
1944 0 0.0 0.0
1945 1 6.2 6.2
1946 0 0.0 0.0
1947 0 0.0 0.0
1948 1 6.2 6.2
1949 2 12.5 12.5
1950 1 6.2 6.2
1951 0 0.0 0.0
1952 1 6.2 6.2
1953 1 6.2 6.2
1954 1 6.2 6.2
1955 1 6.2 6.2
1956 3 18.8 18.8
1957 0 0.0 0.0
1958 0 0.0 0.0
1959 1 6.2 6.2
1960 1 6.2 6.2
1961 0 0.0 0.0
1962 1 6.2 6.2
1963 0 0.0 0.0
1964 0 0.0 0.0
1965 0 0.0 0.0
1966 1 6.2 6.2
1967 0 0.0 0.0
1968 0 0.0 0.0
1969 0 0.0 0.0
1970 0 0.0 0.0
1971 0 0.0 0.0
1972 0 0.0 0.0
1973 0 0.0 0.0
1974 0 0.0 0.0
1976 0 0.0 0.0
1977 0 0.0 0.0
1978 0 0.0 0.0
2015 0 0.0 0.0
2018 0 0.0 0.0
748 0 0.0 0.0
“NA” 0 0.0 0.0
Total 16 100.0 100.0

A5

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other

All data

a5 <- as.factor(d[,"a5"])
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 3310 93.1 93.3
Africa 101 2.8 2.8
Cuba_Caribbean 84 2.4 2.4
Other 40 1.1 1.1
Scantron_Error 11 0.3 0.3
NA 11 0.3 NA
Total 3557 100.0 100.0
#a5: other
  a5other <- as.factor(d[,"a5other"])
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "Born place other")
  temp.d <- data.frame (new.d, a5other) 
  
  result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5 other: Where were you born?")
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 1 0.0 0.0
Barbados 1 0.0 0.0
Beaumont Texas 1 0.0 0.0
Belize 1 0.0 0.0
Belize Central America 1 0.0 0.0
Belize, Central America 2 0.1 0.1
Buffalo NY 1 0.0 0.0
Central America (Panama) 1 0.0 0.0
Central America Belize 1 0.0 0.0
Chatroux, France 1 0.0 0.0
Chicago IL 1 0.0 0.0
England 1 0.0 0.0
Ethiopia 1 0.0 0.0
Georgia Monroe Walton County 1 0.0 0.0
Germany 2 0.1 0.1
Ghana 2 0.1 0.1
Guinea-CKY 1 0.0 0.0
Guyana 4 0.1 0.1
Guyana: South America 1 0.0 0.0
Haiti 6 0.2 0.2
Heidleburg Germany 1 0.0 0.0
Jackson, Miss. 1 0.0 0.0
Jamaica 9 0.3 0.3
JAMAICA 1 0.0 0.0
Jamaica WI 1 0.0 0.0
Jamaican 1 0.0 0.0
Japan 1 0.0 0.0
Kientra, Morocco 1 0.0 0.0
Kingston, Jamaica 1 0.0 0.0
Liberia 1 0.0 0.0
London England 1 0.0 0.0
Macon County 1 0.0 0.0
Mississippi 1 0.0 0.0
“NA” 3485 98.0 98.0
Nassau Bahamas 2 0.1 0.1
New Orleans, LA 1 0.0 0.0
Nigeria 2 0.1 0.1
Nigeria. 1 0.0 0.0
Panama 1 0.0 0.0
Panama City of Panama 1 0.0 0.0
San Diego, CA 1 0.0 0.0
Sierre Leone 1 0.0 0.0
Southampton, Bermuda 1 0.0 0.0
Trinidad 1 0.0 0.0
Trinidad and Tobago 1 0.0 0.0
Trinidad. 1 0.0 0.0
UK 1 0.0 0.0
United States Texas 1 0.0 0.0
Upson County, GA 1 0.0 0.0
Venezuela 1 0.0 0.0
Venezuelan 1 0.0 0.0
West Indies 1 0.0 0.0
Total 3557 100.0 100.0

LA County

A5: Where were you born?
n % val%
US 293 91.3 92.1
Africa 10 3.1 3.1
Cuba_Caribbean 9 2.8 2.8
Other 4 1.2 1.3
Scantron_Error 2 0.6 0.6
NA 3 0.9 NA
Total 321 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Beaumont Texas 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Buffalo NY 1 0.3 0.3
Central America (Panama) 0 0.0 0.0
Central America Belize 0 0.0 0.0
Chatroux, France 0 0.0 0.0
Chicago IL 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guinea-CKY 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 2 0.6 0.6
Heidleburg Germany 0 0.0 0.0
Jackson, Miss. 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Japan 0 0.0 0.0
Kientra, Morocco 0 0.0 0.0
Kingston, Jamaica 0 0.0 0.0
Liberia 0 0.0 0.0
London England 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 1 0.3 0.3
“NA” 311 96.9 96.9
Nassau Bahamas 0 0.0 0.0
New Orleans, LA 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Panama 1 0.3 0.3
Panama City of Panama 1 0.3 0.3
San Diego, CA 0 0.0 0.0
Sierre Leone 1 0.3 0.3
Southampton, Bermuda 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 1 0.3 0.3
UK 1 0.3 0.3
United States Texas 0 0.0 0.0
Upson County, GA 0 0.0 0.0
Venezuela 0 0.0 0.0
Venezuelan 0 0.0 0.0
West Indies 0 0.0 0.0
Total 321 100.0 100.0

Northern CA

A5: Where were you born?
n % val%
US 192 91.4 91.4
Africa 12 5.7 5.7
Cuba_Caribbean 3 1.4 1.4
Other 3 1.4 1.4
Scantron_Error 0 0.0 0.0
Total 210 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 1 0.5 0.5
Barbados 0 0.0 0.0
Beaumont Texas 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Central America Belize 0 0.0 0.0
Chatroux, France 1 0.5 0.5
Chicago IL 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guinea-CKY 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Heidleburg Germany 0 0.0 0.0
Jackson, Miss. 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Japan 0 0.0 0.0
Kientra, Morocco 0 0.0 0.0
Kingston, Jamaica 0 0.0 0.0
Liberia 0 0.0 0.0
London England 1 0.5 0.5
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
“NA” 206 98.1 98.1
Nassau Bahamas 0 0.0 0.0
New Orleans, LA 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 1 0.5 0.5
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
San Diego, CA 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Southampton, Bermuda 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
UK 0 0.0 0.0
United States Texas 0 0.0 0.0
Upson County, GA 0 0.0 0.0
Venezuela 0 0.0 0.0
Venezuelan 0 0.0 0.0
West Indies 0 0.0 0.0
Total 210 100.0 100.0

Greater CA

A5: Where were you born?
n % val%
US 275 87.3 87.9
Africa 23 7.3 7.3
Cuba_Caribbean 4 1.3 1.3
Other 8 2.5 2.6
Scantron_Error 3 1.0 1.0
NA 2 0.6 NA
Total 315 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Beaumont Texas 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Buffalo NY 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Central America Belize 0 0.0 0.0
Chatroux, France 0 0.0 0.0
Chicago IL 0 0.0 0.0
England 1 0.3 0.3
Ethiopia 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 1 0.3 0.3
Ghana 1 0.3 0.3
Guinea-CKY 0 0.0 0.0
Guyana 1 0.3 0.3
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Heidleburg Germany 0 0.0 0.0
Jackson, Miss. 1 0.3 0.3
Jamaica 1 0.3 0.3
JAMAICA 0 0.0 0.0
Jamaica WI 1 0.3 0.3
Jamaican 1 0.3 0.3
Japan 0 0.0 0.0
Kientra, Morocco 1 0.3 0.3
Kingston, Jamaica 0 0.0 0.0
Liberia 0 0.0 0.0
London England 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
“NA” 301 95.6 95.6
Nassau Bahamas 0 0.0 0.0
New Orleans, LA 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
San Diego, CA 1 0.3 0.3
Sierre Leone 0 0.0 0.0
Southampton, Bermuda 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
UK 0 0.0 0.0
United States Texas 1 0.3 0.3
Upson County, GA 0 0.0 0.0
Venezuela 1 0.3 0.3
Venezuelan 1 0.3 0.3
West Indies 0 0.0 0.0
Total 315 100.0 100.0

Detroit

A5: Where were you born?
n % val%
US 353 99.2 99.4
Africa 1 0.3 0.3
Cuba_Caribbean 1 0.3 0.3
Other 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 0.3 NA
Total 356 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0 0
Barbados 0 0 0
Beaumont Texas 0 0 0
Belize 0 0 0
Belize Central America 0 0 0
Belize, Central America 0 0 0
Buffalo NY 0 0 0
Central America (Panama) 0 0 0
Central America Belize 0 0 0
Chatroux, France 0 0 0
Chicago IL 0 0 0
England 0 0 0
Ethiopia 0 0 0
Georgia Monroe Walton County 0 0 0
Germany 0 0 0
Ghana 0 0 0
Guinea-CKY 0 0 0
Guyana 0 0 0
Guyana: South America 0 0 0
Haiti 0 0 0
Heidleburg Germany 0 0 0
Jackson, Miss. 0 0 0
Jamaica 0 0 0
JAMAICA 0 0 0
Jamaica WI 0 0 0
Jamaican 0 0 0
Japan 0 0 0
Kientra, Morocco 0 0 0
Kingston, Jamaica 0 0 0
Liberia 0 0 0
London England 0 0 0
Macon County 0 0 0
Mississippi 0 0 0
“NA” 356 100 100
Nassau Bahamas 0 0 0
New Orleans, LA 0 0 0
Nigeria 0 0 0
Nigeria. 0 0 0
Panama 0 0 0
Panama City of Panama 0 0 0
San Diego, CA 0 0 0
Sierre Leone 0 0 0
Southampton, Bermuda 0 0 0
Trinidad 0 0 0
Trinidad and Tobago 0 0 0
Trinidad. 0 0 0
UK 0 0 0
United States Texas 0 0 0
Upson County, GA 0 0 0
Venezuela 0 0 0
Venezuelan 0 0 0
West Indies 0 0 0
Total 356 100 100

Louisiana

A5: Where were you born?
n % val%
US 575 98.3 98.5
Africa 2 0.3 0.3
Cuba_Caribbean 6 1.0 1.0
Other 0 0.0 0.0
Scantron_Error 1 0.2 0.2
NA 1 0.2 NA
Total 585 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Beaumont Texas 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Central America Belize 0 0.0 0.0
Chatroux, France 0 0.0 0.0
Chicago IL 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guinea-CKY 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Heidleburg Germany 0 0.0 0.0
Jackson, Miss. 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Japan 0 0.0 0.0
Kientra, Morocco 0 0.0 0.0
Kingston, Jamaica 0 0.0 0.0
Liberia 0 0.0 0.0
London England 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
“NA” 584 99.8 99.8
Nassau Bahamas 0 0.0 0.0
New Orleans, LA 1 0.2 0.2
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
San Diego, CA 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Southampton, Bermuda 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
UK 0 0.0 0.0
United States Texas 0 0.0 0.0
Upson County, GA 0 0.0 0.0
Venezuela 0 0.0 0.0
Venezuelan 0 0.0 0.0
West Indies 0 0.0 0.0
Total 585 100.0 100.0

Georgia

A5: Where were you born?
n % val%
US 1607 91.6 91.8
Africa 53 3.0 3.0
Cuba_Caribbean 60 3.4 3.4
Other 25 1.4 1.4
Scantron_Error 5 0.3 0.3
NA 4 0.2 NA
Total 1754 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 1 0.1 0.1
Beaumont Texas 1 0.1 0.1
Belize 1 0.1 0.1
Belize Central America 1 0.1 0.1
Belize, Central America 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Central America (Panama) 1 0.1 0.1
Central America Belize 1 0.1 0.1
Chatroux, France 0 0.0 0.0
Chicago IL 1 0.1 0.1
England 0 0.0 0.0
Ethiopia 1 0.1 0.1
Georgia Monroe Walton County 1 0.1 0.1
Germany 1 0.1 0.1
Ghana 1 0.1 0.1
Guinea-CKY 1 0.1 0.1
Guyana 3 0.2 0.2
Guyana: South America 1 0.1 0.1
Haiti 4 0.2 0.2
Heidleburg Germany 1 0.1 0.1
Jackson, Miss. 0 0.0 0.0
Jamaica 8 0.5 0.5
JAMAICA 1 0.1 0.1
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Japan 1 0.1 0.1
Kientra, Morocco 0 0.0 0.0
Kingston, Jamaica 1 0.1 0.1
Liberia 1 0.1 0.1
London England 0 0.0 0.0
Macon County 1 0.1 0.1
Mississippi 0 0.0 0.0
“NA” 1711 97.5 97.5
Nassau Bahamas 2 0.1 0.1
New Orleans, LA 0 0.0 0.0
Nigeria 2 0.1 0.1
Nigeria. 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
San Diego, CA 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Southampton, Bermuda 1 0.1 0.1
Trinidad 1 0.1 0.1
Trinidad and Tobago 1 0.1 0.1
Trinidad. 0 0.0 0.0
UK 0 0.0 0.0
United States Texas 0 0.0 0.0
Upson County, GA 1 0.1 0.1
Venezuela 0 0.0 0.0
Venezuelan 0 0.0 0.0
West Indies 1 0.1 0.1
Total 1754 100.0 100.0

Michigan

A5: Where were you born?
n % val%
US 15 93.8 93.8
Africa 0 0.0 0.0
Cuba_Caribbean 1 6.2 6.2
Other 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
A5 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0 0
Barbados 0 0 0
Beaumont Texas 0 0 0
Belize 0 0 0
Belize Central America 0 0 0
Belize, Central America 0 0 0
Buffalo NY 0 0 0
Central America (Panama) 0 0 0
Central America Belize 0 0 0
Chatroux, France 0 0 0
Chicago IL 0 0 0
England 0 0 0
Ethiopia 0 0 0
Georgia Monroe Walton County 0 0 0
Germany 0 0 0
Ghana 0 0 0
Guinea-CKY 0 0 0
Guyana 0 0 0
Guyana: South America 0 0 0
Haiti 0 0 0
Heidleburg Germany 0 0 0
Jackson, Miss. 0 0 0
Jamaica 0 0 0
JAMAICA 0 0 0
Jamaica WI 0 0 0
Jamaican 0 0 0
Japan 0 0 0
Kientra, Morocco 0 0 0
Kingston, Jamaica 0 0 0
Liberia 0 0 0
London England 0 0 0
Macon County 0 0 0
Mississippi 0 0 0
“NA” 16 100 100
Nassau Bahamas 0 0 0
New Orleans, LA 0 0 0
Nigeria 0 0 0
Nigeria. 0 0 0
Panama 0 0 0
Panama City of Panama 0 0 0
San Diego, CA 0 0 0
Sierre Leone 0 0 0
Southampton, Bermuda 0 0 0
Trinidad 0 0 0
Trinidad and Tobago 0 0 0
Trinidad. 0 0 0
UK 0 0 0
United States Texas 0 0 0
Upson County, GA 0 0 0
Venezuela 0 0 0
Venezuelan 0 0 0
West Indies 0 0 0
Total 16 100 100

A6

  • A6. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other

All data

a6 <- as.factor(d[,"a6"])
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 3271 92.0 92.8
Africa 101 2.8 2.9
Cuba_Caribbean 95 2.7 2.7
Other 51 1.4 1.4
Scantron_Error 6 0.2 0.2
NA 33 0.9 NA
Total 3557 100.0 100.0
#a6: other
  a6other <- as.factor(d[,"a6other"])
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "Born place other")
  temp.d <- data.frame (new.d, a6other) 
  
  result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6 other: Where were you born?")
a6 other: Where were you born?
n % val%
Barbados 1 0.0 0.0
Belize 1 0.0 0.0
Belize Central America 1 0.0 0.0
Belize, Central America 2 0.1 0.1
Biological father unknown 1 0.0 0.0
Blackstone, VA 1 0.0 0.0
Bombay. 1 0.0 0.0
British Honduras. 1 0.0 0.0
Canada 1 0.0 0.0
Canada. 1 0.0 0.0
Central America (Panama) 1 0.0 0.0
Cleveland Mississippi 1 0.0 0.0
Dead 1 0.0 0.0
Don’t know 2 0.1 0.1
East Pakistan 1 0.0 0.0
England 1 0.0 0.0
Ethiopia 1 0.0 0.0
Father’s birthplace is unknown. 1 0.0 0.0
Georgia Monroe Walton County 1 0.0 0.0
Germany 1 0.0 0.0
Ghana 1 0.0 0.0
Guyana 4 0.1 0.1
Guyana: South America 1 0.0 0.0
Haiti 6 0.2 0.2
Honduras, Central America 1 0.0 0.0
I don’t know 1 0.0 0.0
I was adopted, no info 1 0.0 0.0
Jamaica 11 0.3 0.3
JAMAICA 1 0.0 0.0
Jamaica WI 1 0.0 0.0
Jamaican 1 0.0 0.0
Macon County 1 0.0 0.0
Mississippi 2 0.1 0.1
Montserrat British VI 1 0.0 0.0
“NA” 3478 97.8 97.8
Nassau Bahamas 2 0.1 0.1
Never knew my father 1 0.0 0.0
Nigeria 2 0.1 0.1
Nigeria. 1 0.0 0.0
Not known 1 0.0 0.0
Panama 2 0.1 0.1
Panama-Central America 1 0.0 0.0
Panama Canal Zone 1 0.0 0.0
Panama City of Panama 1 0.0 0.0
Sierre Leone 1 0.0 0.0
Trinidad 1 0.0 0.0
Trinidad and Tobago 1 0.0 0.0
Trinidad. 1 0.0 0.0
United S Texas 1 0.0 0.0
Unknown 4 0.1 0.1
Venezuela 1 0.0 0.0
west Indies 1 0.0 0.0
Yatesville GA 1 0.0 0.0
Total 3557 100.0 100.0

LA County

a6: Where were you born?
n % val%
US 293 91.3 91.6
Africa 10 3.1 3.1
Cuba_Caribbean 10 3.1 3.1
Other 5 1.6 1.6
Scantron_Error 2 0.6 0.6
NA 1 0.3 NA
Total 321 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Biological father unknown 0 0.0 0.0
Blackstone, VA 0 0.0 0.0
Bombay. 0 0.0 0.0
British Honduras. 0 0.0 0.0
Canada 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Cleveland Mississippi 1 0.3 0.3
Dead 0 0.0 0.0
Don’t know 0 0.0 0.0
East Pakistan 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Father’s birthplace is unknown. 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 2 0.6 0.6
Honduras, Central America 0 0.0 0.0
I don’t know 0 0.0 0.0
I was adopted, no info 0 0.0 0.0
Jamaica 2 0.6 0.6
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 311 96.9 96.9
Nassau Bahamas 0 0.0 0.0
Never knew my father 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Not known 0 0.0 0.0
Panama 0 0.0 0.0
Panama-Central America 0 0.0 0.0
Panama Canal Zone 0 0.0 0.0
Panama City of Panama 1 0.3 0.3
Sierre Leone 1 0.3 0.3
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 1 0.3 0.3
United S Texas 0 0.0 0.0
Unknown 1 0.3 0.3
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Yatesville GA 0 0.0 0.0
Total 321 100.0 100.0

Northern CA

a6: Where were you born?
n % val%
US 189 90.0 91.3
Africa 12 5.7 5.8
Cuba_Caribbean 4 1.9 1.9
Other 2 1.0 1.0
Scantron_Error 0 0.0 0.0
NA 3 1.4 NA
Total 210 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Biological father unknown 0 0.0 0.0
Blackstone, VA 0 0.0 0.0
Bombay. 1 0.5 0.5
British Honduras. 0 0.0 0.0
Canada 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Cleveland Mississippi 0 0.0 0.0
Dead 0 0.0 0.0
Don’t know 0 0.0 0.0
East Pakistan 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Father’s birthplace is unknown. 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Honduras, Central America 0 0.0 0.0
I don’t know 0 0.0 0.0
I was adopted, no info 0 0.0 0.0
Jamaica 1 0.5 0.5
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 205 97.6 97.6
Nassau Bahamas 0 0.0 0.0
Never knew my father 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 1 0.5 0.5
Not known 0 0.0 0.0
Panama 1 0.5 0.5
Panama-Central America 0 0.0 0.0
Panama Canal Zone 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
United S Texas 0 0.0 0.0
Unknown 1 0.5 0.5
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Yatesville GA 0 0.0 0.0
Total 210 100.0 100.0

Greater CA

a6: Where were you born?
n % val%
US 270 85.7 87.1
Africa 23 7.3 7.4
Cuba_Caribbean 7 2.2 2.3
Other 9 2.9 2.9
Scantron_Error 1 0.3 0.3
NA 5 1.6 NA
Total 315 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Biological father unknown 0 0.0 0.0
Blackstone, VA 1 0.3 0.3
Bombay. 0 0.0 0.0
British Honduras. 0 0.0 0.0
Canada 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Cleveland Mississippi 0 0.0 0.0
Dead 0 0.0 0.0
Don’t know 1 0.3 0.3
East Pakistan 0 0.0 0.0
England 1 0.3 0.3
Ethiopia 0 0.0 0.0
Father’s birthplace is unknown. 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 1 0.3 0.3
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Honduras, Central America 0 0.0 0.0
I don’t know 0 0.0 0.0
I was adopted, no info 0 0.0 0.0
Jamaica 1 0.3 0.3
JAMAICA 0 0.0 0.0
Jamaica WI 1 0.3 0.3
Jamaican 1 0.3 0.3
Macon County 0 0.0 0.0
Mississippi 1 0.3 0.3
Montserrat British VI 0 0.0 0.0
“NA” 302 95.9 95.9
Nassau Bahamas 0 0.0 0.0
Never knew my father 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Not known 1 0.3 0.3
Panama 1 0.3 0.3
Panama-Central America 0 0.0 0.0
Panama Canal Zone 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
United S Texas 1 0.3 0.3
Unknown 0 0.0 0.0
Venezuela 1 0.3 0.3
west Indies 0 0.0 0.0
Yatesville GA 0 0.0 0.0
Total 315 100.0 100.0

Detroit

a6: Where were you born?
n % val%
US 345 96.9 98.3
Africa 1 0.3 0.3
Cuba_Caribbean 2 0.6 0.6
Other 3 0.8 0.9
Scantron_Error 0 0.0 0.0
NA 5 1.4 NA
Total 356 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Biological father unknown 0 0.0 0.0
Blackstone, VA 0 0.0 0.0
Bombay. 0 0.0 0.0
British Honduras. 0 0.0 0.0
Canada 1 0.3 0.3
Canada. 1 0.3 0.3
Central America (Panama) 0 0.0 0.0
Cleveland Mississippi 0 0.0 0.0
Dead 0 0.0 0.0
Don’t know 0 0.0 0.0
East Pakistan 1 0.3 0.3
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Father’s birthplace is unknown. 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Honduras, Central America 0 0.0 0.0
I don’t know 0 0.0 0.0
I was adopted, no info 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 352 98.9 98.9
Nassau Bahamas 0 0.0 0.0
Never knew my father 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Not known 0 0.0 0.0
Panama 0 0.0 0.0
Panama-Central America 0 0.0 0.0
Panama Canal Zone 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
United S Texas 0 0.0 0.0
Unknown 1 0.3 0.3
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Yatesville GA 0 0.0 0.0
Total 356 100.0 100.0

Louisiana

a6: Where were you born?
n % val%
US 574 98.1 98.5
Africa 2 0.3 0.3
Cuba_Caribbean 5 0.9 0.9
Other 2 0.3 0.3
Scantron_Error 0 0.0 0.0
NA 2 0.3 NA
Total 585 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Biological father unknown 0 0.0 0.0
Blackstone, VA 0 0.0 0.0
Bombay. 0 0.0 0.0
British Honduras. 1 0.2 0.2
Canada 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Panama) 0 0.0 0.0
Cleveland Mississippi 0 0.0 0.0
Dead 0 0.0 0.0
Don’t know 0 0.0 0.0
East Pakistan 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Father’s birthplace is unknown. 0 0.0 0.0
Georgia Monroe Walton County 0 0.0 0.0
Germany 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Honduras, Central America 0 0.0 0.0
I don’t know 0 0.0 0.0
I was adopted, no info 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Macon County 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 583 99.7 99.7
Nassau Bahamas 0 0.0 0.0
Never knew my father 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
Not known 0 0.0 0.0
Panama 0 0.0 0.0
Panama-Central America 0 0.0 0.0
Panama Canal Zone 1 0.2 0.2
Panama City of Panama 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad. 0 0.0 0.0
United S Texas 0 0.0 0.0
Unknown 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Yatesville GA 0 0.0 0.0
Total 585 100.0 100.0

Georgia

a6: Where were you born?
n % val%
US 1585 90.4 91.2
Africa 53 3.0 3.1
Cuba_Caribbean 66 3.8 3.8
Other 30 1.7 1.7
Scantron_Error 3 0.2 0.2
NA 17 1.0 NA
Total 1754 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 1 0.1 0.1
Belize 1 0.1 0.1
Belize Central America 1 0.1 0.1
Belize, Central America 0 0.0 0.0
Biological father unknown 1 0.1 0.1
Blackstone, VA 0 0.0 0.0
Bombay. 0 0.0 0.0
British Honduras. 0 0.0 0.0
Canada 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Panama) 1 0.1 0.1
Cleveland Mississippi 0 0.0 0.0
Dead 1 0.1 0.1
Don’t know 1 0.1 0.1
East Pakistan 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 1 0.1 0.1
Father’s birthplace is unknown. 1 0.1 0.1
Georgia Monroe Walton County 1 0.1 0.1
Germany 1 0.1 0.1
Ghana 1 0.1 0.1
Guyana 3 0.2 0.2
Guyana: South America 1 0.1 0.1
Haiti 4 0.2 0.2
Honduras, Central America 1 0.1 0.1
I don’t know 1 0.1 0.1
I was adopted, no info 1 0.1 0.1
Jamaica 7 0.4 0.4
JAMAICA 1 0.1 0.1
Jamaica WI 0 0.0 0.0
Jamaican 0 0.0 0.0
Macon County 1 0.1 0.1
Mississippi 1 0.1 0.1
Montserrat British VI 1 0.1 0.1
“NA” 1709 97.4 97.4
Nassau Bahamas 2 0.1 0.1
Never knew my father 1 0.1 0.1
Nigeria 2 0.1 0.1
Nigeria. 0 0.0 0.0
Not known 0 0.0 0.0
Panama 0 0.0 0.0
Panama-Central America 1 0.1 0.1
Panama Canal Zone 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 1 0.1 0.1
Trinidad and Tobago 1 0.1 0.1
Trinidad. 0 0.0 0.0
United S Texas 0 0.0 0.0
Unknown 1 0.1 0.1
Venezuela 0 0.0 0.0
west Indies 1 0.1 0.1
Yatesville GA 1 0.1 0.1
Total 1754 100.0 100.0

Michigan

a6: Where were you born?
n % val%
US 15 93.8 93.8
Africa 0 0.0 0.0
Cuba_Caribbean 1 6.2 6.2
Other 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
a6 other: Where were you born?
n % val%
Barbados 0 0 0
Belize 0 0 0
Belize Central America 0 0 0
Belize, Central America 0 0 0
Biological father unknown 0 0 0
Blackstone, VA 0 0 0
Bombay. 0 0 0
British Honduras. 0 0 0
Canada 0 0 0
Canada. 0 0 0
Central America (Panama) 0 0 0
Cleveland Mississippi 0 0 0
Dead 0 0 0
Don’t know 0 0 0
East Pakistan 0 0 0
England 0 0 0
Ethiopia 0 0 0
Father’s birthplace is unknown. 0 0 0
Georgia Monroe Walton County 0 0 0
Germany 0 0 0
Ghana 0 0 0
Guyana 0 0 0
Guyana: South America 0 0 0
Haiti 0 0 0
Honduras, Central America 0 0 0
I don’t know 0 0 0
I was adopted, no info 0 0 0
Jamaica 0 0 0
JAMAICA 0 0 0
Jamaica WI 0 0 0
Jamaican 0 0 0
Macon County 0 0 0
Mississippi 0 0 0
Montserrat British VI 0 0 0
“NA” 16 100 100
Nassau Bahamas 0 0 0
Never knew my father 0 0 0
Nigeria 0 0 0
Nigeria. 0 0 0
Not known 0 0 0
Panama 0 0 0
Panama-Central America 0 0 0
Panama Canal Zone 0 0 0
Panama City of Panama 0 0 0
Sierre Leone 0 0 0
Trinidad 0 0 0
Trinidad and Tobago 0 0 0
Trinidad. 0 0 0
United S Texas 0 0 0
Unknown 0 0 0
Venezuela 0 0 0
west Indies 0 0 0
Yatesville GA 0 0 0
Total 16 100 100

A7

  • A7. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other

All data

a7 <- as.factor(d[,"a7"])
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 3296 92.7 93.2
Africa 98 2.8 2.8
Cuba_Caribbean 96 2.7 2.7
Other 42 1.2 1.2
Scantron_Error 5 0.1 0.1
NA 20 0.6 NA
Total 3557 100.0 100.0
#a7: other
  a7other <- as.factor(d[,"a7other"])
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "Born place other")
  temp.d <- data.frame (new.d, a7other) 
  
  result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7 other: Where were you born?")
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 1 0.0 0.0
Barbados 1 0.0 0.0
Belize 1 0.0 0.0
Belize Central America 1 0.0 0.0
Belize, Central America 2 0.1 0.1
Bremont, TX 1 0.0 0.0
Buffalo NY 1 0.0 0.0
Canada. 1 0.0 0.0
Central America (Colon, Panama) 1 0.0 0.0
England 1 0.0 0.0
Ethiopia 1 0.0 0.0
Georgia Good Hope Walton County 1 0.0 0.0
Ghana 1 0.0 0.0
Guyana 4 0.1 0.1
Guyana: South America 1 0.0 0.0
Haiti 6 0.2 0.2
Jamaica 9 0.3 0.3
JAMAICA 1 0.0 0.0
Jamaica BWI Kingston 1 0.0 0.0
Jamaica WI 1 0.0 0.0
Japan 3 0.1 0.1
LA 1 0.0 0.0
Lamar County, GA 1 0.0 0.0
Macon County 1 0.0 0.0
Mexico 1 0.0 0.0
Mississippi 2 0.1 0.1
Montserrat British VI 1 0.0 0.0
“NA” 3490 98.1 98.1
Nassau Bahamas 1 0.0 0.0
Nigeria 2 0.1 0.1
Nigeria. 1 0.0 0.0
No biological info, I was adopted. 1 0.0 0.0
Oklahoma 1 0.0 0.0
Panama 3 0.1 0.1
Panama City of Panama 1 0.0 0.0
Puerto Rican 1 0.0 0.0
Sierre Leone 1 0.0 0.0
Trinidad 1 0.0 0.0
Trinidad and Tobago 1 0.0 0.0
Trinidad West Indies 1 0.0 0.0
Trinidad. 1 0.0 0.0
United States Texas 1 0.0 0.0
Venezuela 2 0.1 0.1
west Indies 1 0.0 0.0
Total 3557 100.0 100.0

LA County

a7: Where were you born?
n % val%
US 291 90.7 91.2
Africa 10 3.1 3.1
Cuba_Caribbean 11 3.4 3.4
Other 5 1.6 1.6
Scantron_Error 2 0.6 0.6
NA 2 0.6 NA
Total 321 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Bremont, TX 0 0.0 0.0
Buffalo NY 1 0.3 0.3
Canada. 0 0.0 0.0
Central America (Colon, Panama) 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Good Hope Walton County 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 2 0.6 0.6
Jamaica 1 0.3 0.3
JAMAICA 0 0.0 0.0
Jamaica BWI Kingston 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Japan 0 0.0 0.0
LA 0 0.0 0.0
Lamar County, GA 0 0.0 0.0
Macon County 0 0.0 0.0
Mexico 1 0.3 0.3
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 311 96.9 96.9
Nassau Bahamas 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
No biological info, I was adopted. 0 0.0 0.0
Oklahoma 0 0.0 0.0
Panama 1 0.3 0.3
Panama City of Panama 1 0.3 0.3
Puerto Rican 0 0.0 0.0
Sierre Leone 1 0.3 0.3
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad West Indies 0 0.0 0.0
Trinidad. 1 0.3 0.3
United States Texas 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Total 321 100.0 100.0

Northern CA

a7: Where were you born?
n % val%
US 192 91.4 91.4
Africa 12 5.7 5.7
Cuba_Caribbean 4 1.9 1.9
Other 2 1.0 1.0
Scantron_Error 0 0.0 0.0
Total 210 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 1 0.5 0.5
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Bremont, TX 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Colon, Panama) 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Good Hope Walton County 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Jamaica 1 0.5 0.5
JAMAICA 0 0.0 0.0
Jamaica BWI Kingston 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Japan 0 0.0 0.0
LA 0 0.0 0.0
Lamar County, GA 0 0.0 0.0
Macon County 0 0.0 0.0
Mexico 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 207 98.6 98.6
Nassau Bahamas 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 1 0.5 0.5
No biological info, I was adopted. 0 0.0 0.0
Oklahoma 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Puerto Rican 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad West Indies 0 0.0 0.0
Trinidad. 0 0.0 0.0
United States Texas 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Total 210 100.0 100.0

Greater CA

a7: Where were you born?
n % val%
US 269 85.4 87.6
Africa 22 7.0 7.2
Cuba_Caribbean 6 1.9 2.0
Other 9 2.9 2.9
Scantron_Error 1 0.3 0.3
NA 8 2.5 NA
Total 315 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 1 0.3 0.3
Bremont, TX 1 0.3 0.3
Buffalo NY 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Colon, Panama) 0 0.0 0.0
England 1 0.3 0.3
Ethiopia 0 0.0 0.0
Georgia Good Hope Walton County 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 1 0.3 0.3
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica BWI Kingston 0 0.0 0.0
Jamaica WI 1 0.3 0.3
Japan 1 0.3 0.3
LA 0 0.0 0.0
Lamar County, GA 0 0.0 0.0
Macon County 0 0.0 0.0
Mexico 0 0.0 0.0
Mississippi 1 0.3 0.3
Montserrat British VI 0 0.0 0.0
“NA” 302 95.9 95.9
Nassau Bahamas 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
No biological info, I was adopted. 0 0.0 0.0
Oklahoma 1 0.3 0.3
Panama 1 0.3 0.3
Panama City of Panama 0 0.0 0.0
Puerto Rican 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad West Indies 1 0.3 0.3
Trinidad. 0 0.0 0.0
United States Texas 1 0.3 0.3
Venezuela 2 0.6 0.6
west Indies 0 0.0 0.0
Total 315 100.0 100.0

Detroit

a7: Where were you born?
n % val%
US 349 98.0 98.6
Africa 1 0.3 0.3
Cuba_Caribbean 2 0.6 0.6
Other 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 356 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Bremont, TX 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Canada. 1 0.3 0.3
Central America (Colon, Panama) 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Good Hope Walton County 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica BWI Kingston 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Japan 1 0.3 0.3
LA 0 0.0 0.0
Lamar County, GA 0 0.0 0.0
Macon County 0 0.0 0.0
Mexico 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 354 99.4 99.4
Nassau Bahamas 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
No biological info, I was adopted. 0 0.0 0.0
Oklahoma 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Puerto Rican 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad West Indies 0 0.0 0.0
Trinidad. 0 0.0 0.0
United States Texas 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Total 356 100.0 100.0

Louisiana

a7: Where were you born?
n % val%
US 576 98.5 98.8
Africa 1 0.2 0.2
Cuba_Caribbean 6 1.0 1.0
Other 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 2 0.3 NA
Total 585 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 0 0.0 0.0
Belize 0 0.0 0.0
Belize Central America 0 0.0 0.0
Belize, Central America 0 0.0 0.0
Bremont, TX 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Colon, Panama) 0 0.0 0.0
England 0 0.0 0.0
Ethiopia 0 0.0 0.0
Georgia Good Hope Walton County 0 0.0 0.0
Ghana 0 0.0 0.0
Guyana 0 0.0 0.0
Guyana: South America 0 0.0 0.0
Haiti 0 0.0 0.0
Jamaica 0 0.0 0.0
JAMAICA 0 0.0 0.0
Jamaica BWI Kingston 0 0.0 0.0
Jamaica WI 0 0.0 0.0
Japan 0 0.0 0.0
LA 1 0.2 0.2
Lamar County, GA 0 0.0 0.0
Macon County 0 0.0 0.0
Mexico 0 0.0 0.0
Mississippi 0 0.0 0.0
Montserrat British VI 0 0.0 0.0
“NA” 584 99.8 99.8
Nassau Bahamas 0 0.0 0.0
Nigeria 0 0.0 0.0
Nigeria. 0 0.0 0.0
No biological info, I was adopted. 0 0.0 0.0
Oklahoma 0 0.0 0.0
Panama 0 0.0 0.0
Panama City of Panama 0 0.0 0.0
Puerto Rican 0 0.0 0.0
Sierre Leone 0 0.0 0.0
Trinidad 0 0.0 0.0
Trinidad and Tobago 0 0.0 0.0
Trinidad West Indies 0 0.0 0.0
Trinidad. 0 0.0 0.0
United States Texas 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 0 0.0 0.0
Total 585 100.0 100.0

Georgia

a7: Where were you born?
n % val%
US 1604 91.4 91.8
Africa 52 3.0 3.0
Cuba_Caribbean 66 3.8 3.8
Other 24 1.4 1.4
Scantron_Error 2 0.1 0.1
NA 6 0.3 NA
Total 1754 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0.0 0.0
Barbados 1 0.1 0.1
Belize 1 0.1 0.1
Belize Central America 1 0.1 0.1
Belize, Central America 0 0.0 0.0
Bremont, TX 0 0.0 0.0
Buffalo NY 0 0.0 0.0
Canada. 0 0.0 0.0
Central America (Colon, Panama) 1 0.1 0.1
England 0 0.0 0.0
Ethiopia 1 0.1 0.1
Georgia Good Hope Walton County 1 0.1 0.1
Ghana 1 0.1 0.1
Guyana 3 0.2 0.2
Guyana: South America 1 0.1 0.1
Haiti 4 0.2 0.2
Jamaica 7 0.4 0.4
JAMAICA 1 0.1 0.1
Jamaica BWI Kingston 1 0.1 0.1
Jamaica WI 0 0.0 0.0
Japan 1 0.1 0.1
LA 0 0.0 0.0
Lamar County, GA 1 0.1 0.1
Macon County 1 0.1 0.1
Mexico 0 0.0 0.0
Mississippi 1 0.1 0.1
Montserrat British VI 1 0.1 0.1
“NA” 1716 97.8 97.8
Nassau Bahamas 1 0.1 0.1
Nigeria 2 0.1 0.1
Nigeria. 0 0.0 0.0
No biological info, I was adopted. 1 0.1 0.1
Oklahoma 0 0.0 0.0
Panama 1 0.1 0.1
Panama City of Panama 0 0.0 0.0
Puerto Rican 1 0.1 0.1
Sierre Leone 0 0.0 0.0
Trinidad 1 0.1 0.1
Trinidad and Tobago 1 0.1 0.1
Trinidad West Indies 0 0.0 0.0
Trinidad. 0 0.0 0.0
United States Texas 0 0.0 0.0
Venezuela 0 0.0 0.0
west Indies 1 0.1 0.1
Total 1754 100.0 100.0

Michigan

a7: Where were you born?
n % val%
US 15 93.8 93.8
Africa 0 0.0 0.0
Cuba_Caribbean 1 6.2 6.2
Other 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
a7 other: Where were you born?
n % val%
Achtal, Bavaria, Germany 0 0 0
Barbados 0 0 0
Belize 0 0 0
Belize Central America 0 0 0
Belize, Central America 0 0 0
Bremont, TX 0 0 0
Buffalo NY 0 0 0
Canada. 0 0 0
Central America (Colon, Panama) 0 0 0
England 0 0 0
Ethiopia 0 0 0
Georgia Good Hope Walton County 0 0 0
Ghana 0 0 0
Guyana 0 0 0
Guyana: South America 0 0 0
Haiti 0 0 0
Jamaica 0 0 0
JAMAICA 0 0 0
Jamaica BWI Kingston 0 0 0
Jamaica WI 0 0 0
Japan 0 0 0
LA 0 0 0
Lamar County, GA 0 0 0
Macon County 0 0 0
Mexico 0 0 0
Mississippi 0 0 0
Montserrat British VI 0 0 0
“NA” 16 100 100
Nassau Bahamas 0 0 0
Nigeria 0 0 0
Nigeria. 0 0 0
No biological info, I was adopted. 0 0 0
Oklahoma 0 0 0
Panama 0 0 0
Panama City of Panama 0 0 0
Puerto Rican 0 0 0
Sierre Leone 0 0 0
Trinidad 0 0 0
Trinidad and Tobago 0 0 0
Trinidad West Indies 0 0 0
Trinidad. 0 0 0
United States Texas 0 0 0
Venezuela 0 0 0
west Indies 0 0 0
Total 16 100 100

A8

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years

All data

a8 <- as.factor(d[,"a8"])
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3",
                   Scantron_Error="*")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life","Scantron_Error"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 25 0.7 0.7
years_16_25 52 1.5 1.5
more_than_25_or_whole_life 3400 95.6 97.8
Scantron_Error 1 0.0 0.0
NA 79 2.2 NA
Total 3557 100.0 100.0

LA County

A8
n % val%
less_or_15 2 0.6 0.6
years_16_25 3 0.9 0.9
more_than_25_or_whole_life 315 98.1 98.4
Scantron_Error 0 0.0 0.0
NA 1 0.3 NA
Total 321 100.0 100.0

Northern CA

A8
n % val%
less_or_15 4 1.9 1.9
years_16_25 4 1.9 1.9
more_than_25_or_whole_life 198 94.3 96.1
Scantron_Error 0 0.0 0.0
NA 4 1.9 NA
Total 210 100.0 100.0

Greater CA

A8
n % val%
less_or_15 6 1.9 2.0
years_16_25 4 1.3 1.3
more_than_25_or_whole_life 297 94.3 96.7
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 315 100.0 100.0

Detroit

A8
n % val%
less_or_15 0 0.0 0.0
years_16_25 2 0.6 0.6
more_than_25_or_whole_life 342 96.1 99.1
Scantron_Error 1 0.3 0.3
NA 11 3.1 NA
Total 356 100.0 100.0

Louisiana

A8
n % val%
less_or_15 0 0.0 0.0
years_16_25 4 0.7 0.7
more_than_25_or_whole_life 571 97.6 99.3
Scantron_Error 0 0.0 0.0
NA 10 1.7 NA
Total 585 100.0 100.0

Georgia

A8
n % val%
less_or_15 13 0.7 0.8
years_16_25 35 2.0 2.0
more_than_25_or_whole_life 1661 94.7 97.2
Scantron_Error 0 0.0 0.0
NA 45 2.6 NA
Total 1754 100.0 100.0

Michigan

A8
n % val%
less_or_15 0 0 0
years_16_25 0 0 0
more_than_25_or_whole_life 16 100 100
Scantron_Error 0 0 0
Total 16 100 100

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know

All data

# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 1979 55.6 58.2
Yes 649 18.2 19.1
Dont_know 772 21.7 22.7
Scantron_Error 0 0.0 0.0
NA 157 4.4 NA
Total 3557 100.0 100.0
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 692 19.5 58.4
Yes 82 2.3 6.9
Dont_know 408 11.5 34.5
Scantron_Error 2 0.1 0.2
NA 2373 66.7 NA
Total 3557 100.0 100.0
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 755 21.2 62.8
Yes 224 6.3 18.6
Dont_know 223 6.3 18.5
Scantron_Error 1 0.0 0.1
NA 2354 66.2 NA
Total 3557 100.0 100.0

LA County

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 203 63.2 64.2
Yes 51 15.9 16.1
Dont_know 62 19.3 19.6
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 54 16.8 56.2
Yes 5 1.6 5.2
Dont_know 37 11.5 38.5
Scantron_Error 0 0.0 0.0
NA 225 70.1 NA
Total 321 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 61 19.0 64.2
Yes 15 4.7 15.8
Dont_know 18 5.6 18.9
Scantron_Error 1 0.3 1.1
NA 226 70.4 NA
Total 321 100.0 100.0

Northern CA

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 112 53.3 56.3
Yes 48 22.9 24.1
Dont_know 39 18.6 19.6
Scantron_Error 0 0.0 0.0
NA 11 5.2 NA
Total 210 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 43 20.5 65.2
Yes 5 2.4 7.6
Dont_know 18 8.6 27.3
Scantron_Error 0 0.0 0.0
NA 144 68.6 NA
Total 210 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 39 18.6 59.1
Yes 14 6.7 21.2
Dont_know 13 6.2 19.7
Scantron_Error 0 0.0 0.0
NA 144 68.6 NA
Total 210 100.0 100.0

Greater CA

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 159 50.5 52.8
Yes 64 20.3 21.3
Dont_know 78 24.8 25.9
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 67 21.3 62.6
Yes 6 1.9 5.6
Dont_know 34 10.8 31.8
Scantron_Error 0 0.0 0.0
NA 208 66.0 NA
Total 315 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 71 22.5 64.5
Yes 28 8.9 25.5
Dont_know 11 3.5 10.0
Scantron_Error 0 0.0 0.0
NA 205 65.1 NA
Total 315 100.0 100.0

Detroit

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 204 57.3 59.8
Yes 64 18.0 18.8
Dont_know 73 20.5 21.4
Scantron_Error 0 0.0 0.0
NA 15 4.2 NA
Total 356 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 68 19.1 57.6
Yes 9 2.5 7.6
Dont_know 41 11.5 34.7
Scantron_Error 0 0.0 0.0
NA 238 66.9 NA
Total 356 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 81 22.8 68.1
Yes 21 5.9 17.6
Dont_know 17 4.8 14.3
Scantron_Error 0 0.0 0.0
NA 237 66.6 NA
Total 356 100.0 100.0

Louisiana

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 338 57.8 60.6
Yes 106 18.1 19.0
Dont_know 114 19.5 20.4
Scantron_Error 0 0.0 0.0
NA 27 4.6 NA
Total 585 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 117 20.0 57.4
Yes 14 2.4 6.9
Dont_know 72 12.3 35.3
Scantron_Error 1 0.2 0.5
NA 381 65.1 NA
Total 585 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 130 22.2 62.5
Yes 37 6.3 17.8
Dont_know 41 7.0 19.7
Scantron_Error 0 0.0 0.0
NA 377 64.4 NA
Total 585 100.0 100.0

Georgia

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 957 54.6 57.3
Yes 309 17.6 18.5
Dont_know 403 23.0 24.1
Scantron_Error 0 0.0 0.0
NA 85 4.8 NA
Total 1754 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 336 19.2 57.6
Yes 41 2.3 7.0
Dont_know 205 11.7 35.2
Scantron_Error 1 0.1 0.2
NA 1171 66.8 NA
Total 1754 100.0 100.0
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 366 20.9 61.5
Yes 106 6.0 17.8
Dont_know 123 7.0 20.7
Scantron_Error 0 0.0 0.0
NA 1159 66.1 NA
Total 1754 100.0 100.0

Michigan

B1Aa: Father: Has this person had prostate cancer?
n % val%
No 6 37.5 37.5
Yes 7 43.8 43.8
Dont_know 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 7 43.8 70
Yes 2 12.5 20
Dont_know 1 6.2 10
Scantron_Error 0 0.0 0
NA 6 37.5 NA
Total 16 100.0 100
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 7 43.8 70
Yes 3 18.8 30
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 6 37.5 NA
Total 16 100.0 100

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know

All data

# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1",
                        Scantron_Error="*")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 321 9 100
Scantron_Error 0 0 0
NA 3236 91 NA
Total 3557 100 100
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 2110 59.3 66.8
Yes 708 19.9 22.4
Dont_know 338 9.5 10.7
Scantron_Error 2 0.1 0.1
NA 399 11.2 NA
Total 3557 100.0 100.0
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2",
                     Scantron_Error="*")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 288 8.1 63.2
Two_or_more 167 4.7 36.6
Scantron_Error 1 0.0 0.2
NA 3101 87.2 NA
Total 3557 100.0 100.0
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 685 19.3 62.8
Yes 167 4.7 15.3
Dont_know 239 6.7 21.9
Scantron_Error 0 0.0 0.0
NA 2466 69.3 NA
Total 3557 100.0 100.0
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 873 24.5 81.4
Yes 84 2.4 7.8
Dont_know 115 3.2 10.7
Scantron_Error 0 0.0 0.0
NA 2485 69.9 NA
Total 3557 100.0 100.0

LA County

B1BNo: Any Brother
n % val%
No_brothers 29 9 100
Scantron_Error 0 0 0
NA 292 91 NA
Total 321 100 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 201 62.6 67.7
Yes 70 21.8 23.6
Dont_know 26 8.1 8.8
Scantron_Error 0 0.0 0.0
NA 24 7.5 NA
Total 321 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 26 8.1 65
Two_or_more 14 4.4 35
Scantron_Error 0 0.0 0
NA 281 87.5 NA
Total 321 100.0 100
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 59 18.4 62.1
Yes 18 5.6 18.9
Dont_know 18 5.6 18.9
Scantron_Error 0 0.0 0.0
NA 226 70.4 NA
Total 321 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 80 24.9 84.2
Yes 7 2.2 7.4
Dont_know 8 2.5 8.4
Scantron_Error 0 0.0 0.0
NA 226 70.4 NA
Total 321 100.0 100.0

Northern CA

B1BNo: Any Brother
n % val%
No_brothers 32 15.2 100
Scantron_Error 0 0.0 0
NA 178 84.8 NA
Total 210 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 120 57.1 69.0
Yes 28 13.3 16.1
Dont_know 26 12.4 14.9
Scantron_Error 0 0.0 0.0
NA 36 17.1 NA
Total 210 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 19 9.0 79.2
Two_or_more 4 1.9 16.7
Scantron_Error 1 0.5 4.2
NA 186 88.6 NA
Total 210 100.0 100.0
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 28 13.3 57.1
Yes 7 3.3 14.3
Dont_know 14 6.7 28.6
Scantron_Error 0 0.0 0.0
NA 161 76.7 NA
Total 210 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 35 16.7 76.1
Yes 1 0.5 2.2
Dont_know 10 4.8 21.7
Scantron_Error 0 0.0 0.0
NA 164 78.1 NA
Total 210 100.0 100.0

Greater CA

B1BNo: Any Brother
n % val%
No_brothers 28 8.9 100
Scantron_Error 0 0.0 0
NA 287 91.1 NA
Total 315 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 177 56.2 63.9
Yes 72 22.9 26.0
Dont_know 28 8.9 10.1
Scantron_Error 0 0.0 0.0
NA 38 12.1 NA
Total 315 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 30 9.5 65.2
Two_or_more 16 5.1 34.8
Scantron_Error 0 0.0 0.0
NA 269 85.4 NA
Total 315 100.0 100.0
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 63 20.0 60.0
Yes 24 7.6 22.9
Dont_know 18 5.7 17.1
Scantron_Error 0 0.0 0.0
NA 210 66.7 NA
Total 315 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 81 25.7 81
Yes 13 4.1 13
Dont_know 6 1.9 6
Scantron_Error 0 0.0 0
NA 215 68.3 NA
Total 315 100.0 100

Detroit

B1BNo: Any Brother
n % val%
No_brothers 35 9.8 100
Scantron_Error 0 0.0 0
NA 321 90.2 NA
Total 356 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 216 60.7 69.2
Yes 64 18.0 20.5
Dont_know 32 9.0 10.3
Scantron_Error 0 0.0 0.0
NA 44 12.4 NA
Total 356 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 26 7.3 61.9
Two_or_more 16 4.5 38.1
Scantron_Error 0 0.0 0.0
NA 314 88.2 NA
Total 356 100.0 100.0
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 63 17.7 58.9
Yes 14 3.9 13.1
Dont_know 30 8.4 28.0
Scantron_Error 0 0.0 0.0
NA 249 69.9 NA
Total 356 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 83 23.3 79.8
Yes 10 2.8 9.6
Dont_know 11 3.1 10.6
Scantron_Error 0 0.0 0.0
NA 252 70.8 NA
Total 356 100.0 100.0

Louisiana

B1BNo: Any Brother
n % val%
No_brothers 31 5.3 100
Scantron_Error 0 0.0 0
NA 554 94.7 NA
Total 585 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 373 63.8 68.7
Yes 114 19.5 21.0
Dont_know 56 9.6 10.3
Scantron_Error 0 0.0 0.0
NA 42 7.2 NA
Total 585 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 42 7.2 60.9
Two_or_more 27 4.6 39.1
Scantron_Error 0 0.0 0.0
NA 516 88.2 NA
Total 585 100.0 100.0
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 138 23.6 69.3
Yes 26 4.4 13.1
Dont_know 35 6.0 17.6
Scantron_Error 0 0.0 0.0
NA 386 66.0 NA
Total 585 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 161 27.5 84.3
Yes 10 1.7 5.2
Dont_know 20 3.4 10.5
Scantron_Error 0 0.0 0.0
NA 394 67.4 NA
Total 585 100.0 100.0

Georgia

B1BNo: Any Brother
n % val%
No_brothers 163 9.3 100
Scantron_Error 0 0.0 0
NA 1591 90.7 NA
Total 1754 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 1015 57.9 65.8
Yes 356 20.3 23.1
Dont_know 169 9.6 11.0
Scantron_Error 2 0.1 0.1
NA 212 12.1 NA
Total 1754 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 144 8.2 61.5
Two_or_more 90 5.1 38.5
Scantron_Error 0 0.0 0.0
NA 1520 86.7 NA
Total 1754 100.0 100.0
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 331 18.9 62.3
Yes 76 4.3 14.3
Dont_know 124 7.1 23.4
Scantron_Error 0 0.0 0.0
NA 1223 69.7 NA
Total 1754 100.0 100.0
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 428 24.4 80.8
Yes 43 2.5 8.1
Dont_know 59 3.4 11.1
Scantron_Error 0 0.0 0.0
NA 1224 69.8 NA
Total 1754 100.0 100.0

Michigan

B1BNo: Any Brother
n % val%
No_brothers 3 18.8 100
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 8 50.0 61.5
Yes 4 25.0 30.8
Dont_know 1 6.2 7.7
Scantron_Error 0 0.0 0.0
NA 3 18.8 NA
Total 16 100.0 100.0
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 1 6.2 100
Two_or_more 0 0.0 0
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 3 18.8 60
Yes 2 12.5 40
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 5 31.2 83.3
Yes 0 0.0 0.0
Dont_know 1 6.2 16.7
Scantron_Error 0 0.0 0.0
NA 10 62.5 NA
Total 16 100.0 100.0

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know

All data

# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1",
                        Scantron_Error="*")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 608 17.1 100
Scantron_Error 0 0.0 0
NA 2949 82.9 NA
Total 3557 100.0 100
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 2621 73.7 93.2
Yes 87 2.4 3.1
Dont_know 104 2.9 3.7
Scantron_Error 1 0.0 0.0
NA 744 20.9 NA
Total 3557 100.0 100.0
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2",
                     Scantron_Error="*")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 33 0.9 48.5
Two_or_more 34 1.0 50.0
Scantron_Error 1 0.0 1.5
NA 3489 98.1 NA
Total 3557 100.0 100.0
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 508 14.3 85.1
Yes 12 0.3 2.0
Dont_know 77 2.2 12.9
Scantron_Error 0 0.0 0.0
NA 2960 83.2 NA
Total 3557 100.0 100.0
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 518 14.6 88.5
Yes 3 0.1 0.5
Dont_know 64 1.8 10.9
Scantron_Error 0 0.0 0.0
NA 2972 83.6 NA
Total 3557 100.0 100.0

LA County

B1CNo: Any Son
n % val%
No_brothers 46 14.3 100
Scantron_Error 0 0.0 0
NA 275 85.7 NA
Total 321 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 260 81.0 96.3
Yes 3 0.9 1.1
Dont_know 7 2.2 2.6
Scantron_Error 0 0.0 0.0
NA 51 15.9 NA
Total 321 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 3 0.9 42.9
Two_or_more 4 1.2 57.1
Scantron_Error 0 0.0 0.0
NA 314 97.8 NA
Total 321 100.0 100.0
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 34 10.6 77.3
Yes 2 0.6 4.5
Dont_know 8 2.5 18.2
Scantron_Error 0 0.0 0.0
NA 277 86.3 NA
Total 321 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 33 10.3 84.6
Yes 0 0.0 0.0
Dont_know 6 1.9 15.4
Scantron_Error 0 0.0 0.0
NA 282 87.9 NA
Total 321 100.0 100.0

Northern CA

B1CNo: Any Son
n % val%
No_brothers 47 22.4 100
Scantron_Error 0 0.0 0
NA 163 77.6 NA
Total 210 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 145 69.0 95.4
Yes 4 1.9 2.6
Dont_know 3 1.4 2.0
Scantron_Error 0 0.0 0.0
NA 58 27.6 NA
Total 210 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 3 1.4 42.9
Two_or_more 4 1.9 57.1
Scantron_Error 0 0.0 0.0
NA 203 96.7 NA
Total 210 100.0 100.0
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 19 9.0 79.2
Yes 1 0.5 4.2
Dont_know 4 1.9 16.7
Scantron_Error 0 0.0 0.0
NA 186 88.6 NA
Total 210 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 20 9.5 87.0
Yes 1 0.5 4.3
Dont_know 2 1.0 8.7
Scantron_Error 0 0.0 0.0
NA 187 89.0 NA
Total 210 100.0 100.0

Greater CA

B1CNo: Any Son
n % val%
No_brothers 53 16.8 100
Scantron_Error 0 0.0 0
NA 262 83.2 NA
Total 315 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 237 75.2 92.2
Yes 10 3.2 3.9
Dont_know 10 3.2 3.9
Scantron_Error 0 0.0 0.0
NA 58 18.4 NA
Total 315 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 2 0.6 50
Two_or_more 2 0.6 50
Scantron_Error 0 0.0 0
NA 311 98.7 NA
Total 315 100.0 100
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 50 15.9 92.6
Yes 0 0.0 0.0
Dont_know 4 1.3 7.4
Scantron_Error 0 0.0 0.0
NA 261 82.9 NA
Total 315 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 49 15.6 94.2
Yes 0 0.0 0.0
Dont_know 3 1.0 5.8
Scantron_Error 0 0.0 0.0
NA 263 83.5 NA
Total 315 100.0 100.0

Detroit

B1CNo: Any Son
n % val%
No_brothers 71 19.9 100
Scantron_Error 0 0.0 0
NA 285 80.1 NA
Total 356 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 254 71.3 94.1
Yes 8 2.2 3.0
Dont_know 8 2.2 3.0
Scantron_Error 0 0.0 0.0
NA 86 24.2 NA
Total 356 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 3 0.8 75
Two_or_more 1 0.3 25
Scantron_Error 0 0.0 0
NA 352 98.9 NA
Total 356 100.0 100
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 48 13.5 87.3
Yes 1 0.3 1.8
Dont_know 6 1.7 10.9
Scantron_Error 0 0.0 0.0
NA 301 84.6 NA
Total 356 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 49 13.8 89.1
Yes 1 0.3 1.8
Dont_know 5 1.4 9.1
Scantron_Error 0 0.0 0.0
NA 301 84.6 NA
Total 356 100.0 100.0

Louisiana

B1CNo: Any Son
n % val%
No_brothers 82 14 100
Scantron_Error 0 0 0
NA 503 86 NA
Total 585 100 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 443 75.7 93.1
Yes 15 2.6 3.2
Dont_know 18 3.1 3.8
Scantron_Error 0 0.0 0.0
NA 109 18.6 NA
Total 585 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 4 0.7 40
Two_or_more 5 0.9 50
Scantron_Error 1 0.2 10
NA 575 98.3 NA
Total 585 100.0 100
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 95 16.2 84.8
Yes 1 0.2 0.9
Dont_know 16 2.7 14.3
Scantron_Error 0 0.0 0.0
NA 473 80.9 NA
Total 585 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 94 16.1 86.2
Yes 1 0.2 0.9
Dont_know 14 2.4 12.8
Scantron_Error 0 0.0 0.0
NA 476 81.4 NA
Total 585 100.0 100.0

Georgia

B1CNo: Any Son
n % val%
No_brothers 306 17.4 100
Scantron_Error 0 0.0 0
NA 1448 82.6 NA
Total 1754 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 1270 72.4 92.3
Yes 47 2.7 3.4
Dont_know 58 3.3 4.2
Scantron_Error 1 0.1 0.1
NA 378 21.6 NA
Total 1754 100.0 100.0
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 18 1.0 50
Two_or_more 18 1.0 50
Scantron_Error 0 0.0 0
NA 1718 97.9 NA
Total 1754 100.0 100
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 259 14.8 84.9
Yes 7 0.4 2.3
Dont_know 39 2.2 12.8
Scantron_Error 0 0.0 0.0
NA 1449 82.6 NA
Total 1754 100.0 100.0
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 270 15.4 88.8
Yes 0 0.0 0.0
Dont_know 34 1.9 11.2
Scantron_Error 0 0.0 0.0
NA 1450 82.7 NA
Total 1754 100.0 100.0

Michigan

B1CNo: Any Son
n % val%
No_brothers 3 18.8 100
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 12 75 100
Yes 0 0 0
Dont_know 0 0 0
Scantron_Error 0 0 0
NA 4 25 NA
Total 16 100 100
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 0 0 NaN
Two_or_more 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 3 18.8 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 3 18.8 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know

All data

# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1da <- ordered(b1da, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 1474 41.4 44.6
Yes 104 2.9 3.1
Dont_know 1726 48.5 52.2
Scantron_Error 4 0.1 0.1
NA 249 7.0 NA
Total 3557 100.0 100.0
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1db <- ordered(b1db, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 286 8.0 38.2
Yes 10 0.3 1.3
Dont_know 452 12.7 60.4
Scantron_Error 0 0.0 0.0
NA 2809 79.0 NA
Total 3557 100.0 100.0
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 292 8.2 38.5
Yes 42 1.2 5.5
Dont_know 425 11.9 56.0
Scantron_Error 0 0.0 0.0
NA 2798 78.7 NA
Total 3557 100.0 100.0

LA County

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 161 50.2 51.8
Yes 6 1.9 1.9
Dont_know 143 44.5 46.0
Scantron_Error 1 0.3 0.3
NA 10 3.1 NA
Total 321 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 17 5.3 29.8
Yes 0 0.0 0.0
Dont_know 40 12.5 70.2
Scantron_Error 0 0.0 0.0
NA 264 82.2 NA
Total 321 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 17 5.3 28.8
Yes 3 0.9 5.1
Dont_know 39 12.1 66.1
Scantron_Error 0 0.0 0.0
NA 262 81.6 NA
Total 321 100.0 100.0

Northern CA

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 67 31.9 34.9
Yes 6 2.9 3.1
Dont_know 119 56.7 62.0
Scantron_Error 0 0.0 0.0
NA 18 8.6 NA
Total 210 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 12 5.7 36.4
Yes 1 0.5 3.0
Dont_know 20 9.5 60.6
Scantron_Error 0 0.0 0.0
NA 177 84.3 NA
Total 210 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 13 6.2 39.4
Yes 2 1.0 6.1
Dont_know 18 8.6 54.5
Scantron_Error 0 0.0 0.0
NA 177 84.3 NA
Total 210 100.0 100.0

Greater CA

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 127 40.3 42.5
Yes 10 3.2 3.3
Dont_know 162 51.4 54.2
Scantron_Error 0 0.0 0.0
NA 16 5.1 NA
Total 315 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 30 9.5 42.3
Yes 1 0.3 1.4
Dont_know 40 12.7 56.3
Scantron_Error 0 0.0 0.0
NA 244 77.5 NA
Total 315 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 31 9.8 44.9
Yes 6 1.9 8.7
Dont_know 32 10.2 46.4
Scantron_Error 0 0.0 0.0
NA 246 78.1 NA
Total 315 100.0 100.0

Detroit

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 151 42.4 45.2
Yes 16 4.5 4.8
Dont_know 166 46.6 49.7
Scantron_Error 1 0.3 0.3
NA 22 6.2 NA
Total 356 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 28 7.9 40.6
Yes 2 0.6 2.9
Dont_know 39 11.0 56.5
Scantron_Error 0 0.0 0.0
NA 287 80.6 NA
Total 356 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 29 8.1 42.0
Yes 4 1.1 5.8
Dont_know 36 10.1 52.2
Scantron_Error 0 0.0 0.0
NA 287 80.6 NA
Total 356 100.0 100.0

Louisiana

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 261 44.6 49.1
Yes 21 3.6 3.9
Dont_know 250 42.7 47.0
Scantron_Error 0 0.0 0.0
NA 53 9.1 NA
Total 585 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 58 9.9 40.0
Yes 3 0.5 2.1
Dont_know 84 14.4 57.9
Scantron_Error 0 0.0 0.0
NA 440 75.2 NA
Total 585 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 56 9.6 39.2
Yes 9 1.5 6.3
Dont_know 78 13.3 54.5
Scantron_Error 0 0.0 0.0
NA 442 75.6 NA
Total 585 100.0 100.0

Georgia

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 703 40.1 43.3
Yes 44 2.5 2.7
Dont_know 876 49.9 53.9
Scantron_Error 2 0.1 0.1
NA 129 7.4 NA
Total 1754 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 140 8.0 38.0
Yes 2 0.1 0.5
Dont_know 226 12.9 61.4
Scantron_Error 0 0.0 0.0
NA 1386 79.0 NA
Total 1754 100.0 100.0
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 145 8.3 38.1
Yes 18 1.0 4.7
Dont_know 218 12.4 57.2
Scantron_Error 0 0.0 0.0
NA 1373 78.3 NA
Total 1754 100.0 100.0

Michigan

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 4 25.0 26.7
Yes 1 6.2 6.7
Dont_know 10 62.5 66.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 1 6.2 20
Yes 1 6.2 20
Dont_know 3 18.8 60
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 1 6.2 20
Yes 0 0.0 0
Dont_know 4 25.0 80
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know

All data

# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 1327 37.3 40.5
Yes 97 2.7 3.0
Dont_know 1851 52.0 56.5
Scantron_Error 1 0.0 0.0
NA 281 7.9 NA
Total 3557 100.0 100.0
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 241 6.8 33.1
Yes 15 0.4 2.1
Dont_know 472 13.3 64.8
Scantron_Error 0 0.0 0.0
NA 2829 79.5 NA
Total 3557 100.0 100.0
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 256 7.2 34.2
Yes 47 1.3 6.3
Dont_know 446 12.5 59.5
Scantron_Error 0 0.0 0.0
NA 2808 78.9 NA
Total 3557 100.0 100.0

LA County

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 146 45.5 47.7
Yes 9 2.8 2.9
Dont_know 151 47.0 49.3
Scantron_Error 0 0.0 0.0
NA 15 4.7 NA
Total 321 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 18 5.6 31.6
Yes 0 0.0 0.0
Dont_know 39 12.1 68.4
Scantron_Error 0 0.0 0.0
NA 264 82.2 NA
Total 321 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 20 6.2 33.9
Yes 2 0.6 3.4
Dont_know 37 11.5 62.7
Scantron_Error 0 0.0 0.0
NA 262 81.6 NA
Total 321 100.0 100.0

Northern CA

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 55 26.2 28.6
Yes 11 5.2 5.7
Dont_know 126 60.0 65.6
Scantron_Error 0 0.0 0.0
NA 18 8.6 NA
Total 210 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 12 5.7 28.6
Yes 3 1.4 7.1
Dont_know 27 12.9 64.3
Scantron_Error 0 0.0 0.0
NA 168 80.0 NA
Total 210 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 13 6.2 31.7
Yes 6 2.9 14.6
Dont_know 22 10.5 53.7
Scantron_Error 0 0.0 0.0
NA 169 80.5 NA
Total 210 100.0 100.0

Greater CA

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 109 34.6 36.7
Yes 7 2.2 2.4
Dont_know 181 57.5 60.9
Scantron_Error 0 0.0 0.0
NA 18 5.7 NA
Total 315 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 25 7.9 38.5
Yes 0 0.0 0.0
Dont_know 40 12.7 61.5
Scantron_Error 0 0.0 0.0
NA 250 79.4 NA
Total 315 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 24 7.6 36.4
Yes 2 0.6 3.0
Dont_know 40 12.7 60.6
Scantron_Error 0 0.0 0.0
NA 249 79.0 NA
Total 315 100.0 100.0

Detroit

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 148 41.6 45.0
Yes 10 2.8 3.0
Dont_know 170 47.8 51.7
Scantron_Error 1 0.3 0.3
NA 27 7.6 NA
Total 356 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 26 7.3 41.9
Yes 1 0.3 1.6
Dont_know 35 9.8 56.5
Scantron_Error 0 0.0 0.0
NA 294 82.6 NA
Total 356 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 25 7.0 39.1
Yes 6 1.7 9.4
Dont_know 33 9.3 51.6
Scantron_Error 0 0.0 0.0
NA 292 82.0 NA
Total 356 100.0 100.0

Louisiana

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 238 40.7 45.2
Yes 21 3.6 4.0
Dont_know 267 45.6 50.8
Scantron_Error 0 0.0 0.0
NA 59 10.1 NA
Total 585 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 46 7.9 32.6
Yes 5 0.9 3.5
Dont_know 90 15.4 63.8
Scantron_Error 0 0.0 0.0
NA 444 75.9 NA
Total 585 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 48 8.2 34.5
Yes 11 1.9 7.9
Dont_know 80 13.7 57.6
Scantron_Error 0 0.0 0.0
NA 446 76.2 NA
Total 585 100.0 100.0

Georgia

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 627 35.7 38.9
Yes 38 2.2 2.4
Dont_know 946 53.9 58.7
Scantron_Error 0 0.0 0.0
NA 143 8.2 NA
Total 1754 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 113 6.4 31.7
Yes 6 0.3 1.7
Dont_know 238 13.6 66.7
Scantron_Error 0 0.0 0.0
NA 1397 79.6 NA
Total 1754 100.0 100.0
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 125 7.1 33.2
Yes 19 1.1 5.1
Dont_know 232 13.2 61.7
Scantron_Error 0 0.0 0.0
NA 1378 78.6 NA
Total 1754 100.0 100.0

Michigan

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 4 25.0 26.7
Yes 1 6.2 6.7
Dont_know 10 62.5 66.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 1 6.2 25
Yes 0 0.0 0
Dont_know 3 18.8 75
Scantron_Error 0 0.0 0
NA 12 75.0 NA
Total 16 100.0 100
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 1 6.2 25
Yes 1 6.2 25
Dont_know 2 12.5 50
Scantron_Error 0 0.0 0
NA 12 75.0 NA
Total 16 100.0 100

B3

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor

All data

  b3 <- as.factor(d[,"b3"])
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5",
                     Scantron_Error="*")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor","Scantron_Error"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 221 6.2 6.5 6.2 6.5
Very_Good 933 26.2 27.3 32.4 33.8
Good 1439 40.5 42.1 72.9 75.9
Fair 724 20.4 21.2 93.3 97.1
Poor 95 2.7 2.8 95.9 99.9
Scantron_Error 3 0.1 0.1 96.0 100.0
NA 142 4.0 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

n % val% %cum val%cum
Excellent 27 8.4 8.7 8.4 8.7
Very_Good 102 31.8 32.7 40.2 41.3
Good 120 37.4 38.5 77.6 79.8
Fair 51 15.9 16.3 93.5 96.2
Poor 12 3.7 3.8 97.2 100.0
Scantron_Error 0 0.0 0.0 97.2 100.0
NA 9 2.8 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

n % val% %cum val%cum
Excellent 14 6.7 7.0 6.7 7.0
Very_Good 61 29.0 30.7 35.7 37.7
Good 81 38.6 40.7 74.3 78.4
Fair 41 19.5 20.6 93.8 99.0
Poor 1 0.5 0.5 94.3 99.5
Scantron_Error 1 0.5 0.5 94.8 100.0
NA 11 5.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

n % val% %cum val%cum
Excellent 21 6.7 7.0 6.7 7.0
Very_Good 92 29.2 30.5 35.9 37.4
Good 122 38.7 40.4 74.6 77.8
Fair 58 18.4 19.2 93.0 97.0
Poor 9 2.9 3.0 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

n % val% %cum val%cum
Excellent 20 5.6 5.8 5.6 5.8
Very_Good 72 20.2 21.1 25.8 26.9
Good 145 40.7 42.4 66.6 69.3
Fair 87 24.4 25.4 91.0 94.7
Poor 17 4.8 5.0 95.8 99.7
Scantron_Error 1 0.3 0.3 96.1 100.0
NA 14 3.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

n % val% %cum val%cum
Excellent 29 5.0 5.2 5.0 5.2
Very_Good 135 23.1 24.0 28.0 29.1
Good 236 40.3 41.9 68.4 71.0
Fair 144 24.6 25.6 93.0 96.6
Poor 19 3.2 3.4 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 22 3.8 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

n % val% %cum val%cum
Excellent 109 6.2 6.5 6.2 6.5
Very_Good 466 26.6 27.7 32.8 34.2
Good 728 41.5 43.3 74.3 77.5
Fair 341 19.4 20.3 93.7 97.8
Poor 36 2.1 2.1 95.8 99.9
Scantron_Error 1 0.1 0.1 95.8 100.0
NA 73 4.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

n % val% %cum val%cum
Excellent 1 6.2 6.2 6.2 6.2
Very_Good 5 31.2 31.2 37.5 37.5
Good 7 43.8 43.8 81.2 81.2
Fair 2 12.5 12.5 93.8 93.8
Poor 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0

B4

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer

All data

# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
  levels(b4aa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4aa <- ordered(b4aa, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 3110 87.4 92.8
Yes 238 6.7 7.1
Scantron_Error 2 0.1 0.1
NA 207 5.8 NA
Total 3557 100.0 100.0
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
0 43 1.2 1.2
1 1 0.0 0.0
14 1 0.0 0.0
17 2 0.1 0.1
20 1 0.0 0.0
24 1 0.0 0.0
25 2 0.1 0.1
26 1 0.0 0.0
27 2 0.1 0.1
29 2 0.1 0.1
31 2 0.1 0.1
32 1 0.0 0.0
34 1 0.0 0.0
35 2 0.1 0.1
38 3 0.1 0.1
40 4 0.1 0.1
42 1 0.0 0.0
44 2 0.1 0.1
45 4 0.1 0.1
46 4 0.1 0.1
47 1 0.0 0.0
48 7 0.2 0.2
49 2 0.1 0.1
5 1 0.0 0.0
50 19 0.5 0.5
51 6 0.2 0.2
52 10 0.3 0.3
53 2 0.1 0.1
54 5 0.1 0.1
55 10 0.3 0.3
56 4 0.1 0.1
57 7 0.2 0.2
58 12 0.3 0.3
59 12 0.3 0.3
60 14 0.4 0.4
61 6 0.2 0.2
62 3 0.1 0.1
63 8 0.2 0.2
64 8 0.2 0.2
65 6 0.2 0.2
66 3 0.1 0.1
67 6 0.2 0.2
68 2 0.1 0.1
69 7 0.2 0.2
70 5 0.1 0.1
71 3 0.1 0.1
72 1 0.0 0.0
73 3 0.1 0.1
74 6 0.2 0.2
76 2 0.1 0.1
79 1 0.0 0.0
93 1 0.0 0.0
“NA” 3294 92.6 92.6
Total 3557 100.0 100.0
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  levels(b4ba) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ba <- ordered(b4ba, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 3092 86.9 93.3
Yes 222 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 243 6.8 NA
Total 3557 100.0 100.0
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
12 1 0.0 0.0
13 1 0.0 0.0
17 1 0.0 0.0
18 1 0.0 0.0
20 2 0.1 0.1
25 1 0.0 0.0
27 1 0.0 0.0
28 1 0.0 0.0
29 1 0.0 0.0
30 2 0.1 0.1
31 1 0.0 0.0
34 1 0.0 0.0
35 1 0.0 0.0
39 1 0.0 0.0
40 4 0.1 0.1
42 1 0.0 0.0
43 3 0.1 0.1
45 1 0.0 0.0
46 2 0.1 0.1
47 2 0.1 0.1
48 4 0.1 0.1
49 6 0.2 0.2
5 1 0.0 0.0
50 6 0.2 0.2
51 3 0.1 0.1
52 7 0.2 0.2
53 4 0.1 0.1
54 5 0.1 0.1
55 9 0.3 0.3
56 10 0.3 0.3
57 4 0.1 0.1
58 6 0.2 0.2
59 10 0.3 0.3
60 11 0.3 0.3
61 6 0.2 0.2
62 11 0.3 0.3
63 6 0.2 0.2
64 4 0.1 0.1
65 8 0.2 0.2
66 5 0.1 0.1
67 4 0.1 0.1
68 5 0.1 0.1
69 5 0.1 0.1
7 1 0.0 0.0
70 6 0.2 0.2
71 5 0.1 0.1
72 3 0.1 0.1
73 2 0.1 0.1
74 3 0.1 0.1
75 2 0.1 0.1
77 1 0.0 0.0
78 1 0.0 0.0
80 1 0.0 0.0
82 1 0.0 0.0
97 1 0.0 0.0
“NA” 3361 94.5 94.5
Total 3557 100.0 100.0
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  levels(b4ca) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ca <- ordered(b4ca, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 3009 84.6 90.7
Yes 306 8.6 9.2
Scantron_Error 3 0.1 0.1
NA 239 6.7 NA
Total 3557 100.0 100.0
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
0 67 1.9 1.9
15 1 0.0 0.0
16 1 0.0 0.0
19 1 0.0 0.0
20 1 0.0 0.0
23 1 0.0 0.0
25 1 0.0 0.0
27 1 0.0 0.0
30 2 0.1 0.1
34 2 0.1 0.1
35 1 0.0 0.0
38 1 0.0 0.0
40 4 0.1 0.1
42 4 0.1 0.1
43 3 0.1 0.1
45 7 0.2 0.2
46 4 0.1 0.1
47 2 0.1 0.1
48 4 0.1 0.1
49 1 0.0 0.0
5 3 0.1 0.1
50 11 0.3 0.3
51 5 0.1 0.1
52 7 0.2 0.2
53 3 0.1 0.1
54 11 0.3 0.3
55 11 0.3 0.3
56 7 0.2 0.2
57 12 0.3 0.3
58 7 0.2 0.2
59 10 0.3 0.3
60 16 0.4 0.4
61 14 0.4 0.4
62 11 0.3 0.3
63 19 0.5 0.5
64 9 0.3 0.3
65 15 0.4 0.4
66 9 0.3 0.3
67 7 0.2 0.2
68 10 0.3 0.3
69 10 0.3 0.3
70 6 0.2 0.2
71 3 0.1 0.1
72 2 0.1 0.1
73 2 0.1 0.1
74 3 0.1 0.1
75 1 0.0 0.0
78 2 0.1 0.1
79 3 0.1 0.1
8 1 0.0 0.0
“NA” 3218 90.5 90.5
Total 3557 100.0 100.0
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
  levels(b4da) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4da <- ordered(b4da, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 887 24.9 26.2
Yes 2496 70.2 73.6
Scantron_Error 7 0.2 0.2
NA 167 4.7 NA
Total 3557 100.0 100.0
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
.35 1 0.0 0.0
1 1 0.0 0.0
10 1 0.0 0.0
12 3 0.1 0.1
13 1 0.0 0.0
14 1 0.0 0.0
15 3 0.1 0.1
16 9 0.3 0.3
17 2 0.1 0.1
18 7 0.2 0.2
19 4 0.1 0.1
2 3 0.1 0.1
20 12 0.3 0.3
21 5 0.1 0.1
22 7 0.2 0.2
23 5 0.1 0.1
24 9 0.3 0.3
25 18 0.5 0.5
26 3 0.1 0.1
27 7 0.2 0.2
28 11 0.3 0.3
29 6 0.2 0.2
30 62 1.7 1.7
31 13 0.4 0.4
32 14 0.4 0.4
33 9 0.3 0.3
34 6 0.2 0.2
35 79 2.2 2.2
36 18 0.5 0.5
37 11 0.3 0.3
38 24 0.7 0.7
39 14 0.4 0.4
4 2 0.1 0.1
40 165 4.6 4.6
41 15 0.4 0.4
42 33 0.9 0.9
43 19 0.5 0.5
44 17 0.5 0.5
45 154 4.3 4.3
46 26 0.7 0.7
47 21 0.6 0.6
48 42 1.2 1.2
49 29 0.8 0.8
5 5 0.1 0.1
50 251 7.1 7.1
51 23 0.6 0.6
52 45 1.3 1.3
53 18 0.5 0.5
54 49 1.4 1.4
55 166 4.7 4.7
56 52 1.5 1.5
57 38 1.1 1.1
58 62 1.7 1.7
59 37 1.0 1.0
6 1 0.0 0.0
60 163 4.6 4.6
61 35 1.0 1.0
62 53 1.5 1.5
63 25 0.7 0.7
64 30 0.8 0.8
65 56 1.6 1.6
66 15 0.4 0.4
67 22 0.6 0.6
68 25 0.7 0.7
69 17 0.5 0.5
7 2 0.1 0.1
70 27 0.8 0.8
71 13 0.4 0.4
72 10 0.3 0.3
73 5 0.1 0.1
74 3 0.1 0.1
75 4 0.1 0.1
77 1 0.0 0.0
78 1 0.0 0.0
79 1 0.0 0.0
8 3 0.1 0.1
80 2 0.1 0.1
89 2 0.1 0.1
9 3 0.1 0.1
92 1 0.0 0.0
94 2 0.1 0.1
96 1 0.0 0.0
98 1 0.0 0.0
99 4 0.1 0.1
“NA” 1391 39.1 39.1
Total 3557 100.0 100.0
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
  levels(b4ea) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ea <- ordered(b4ea, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 3031 85.2 93.3
Yes 215 6.0 6.6
Scantron_Error 2 0.1 0.1
NA 309 8.7 NA
Total 3557 100.0 100.0
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
* 1 0.0 0.0
** 1 0.0 0.0
0 23 0.6 0.6
1 1 0.0 0.0
16 1 0.0 0.0
17 1 0.0 0.0
19 1 0.0 0.0
25 1 0.0 0.0
26 1 0.0 0.0
30 2 0.1 0.1
31 1 0.0 0.0
33 1 0.0 0.0
34 2 0.1 0.1
35 4 0.1 0.1
36 1 0.0 0.0
37 1 0.0 0.0
40 12 0.3 0.3
41 1 0.0 0.0
42 1 0.0 0.0
44 2 0.1 0.1
45 5 0.1 0.1
46 1 0.0 0.0
47 1 0.0 0.0
48 7 0.2 0.2
5 1 0.0 0.0
50 19 0.5 0.5
51 4 0.1 0.1
52 1 0.0 0.0
53 2 0.1 0.1
54 6 0.2 0.2
55 13 0.4 0.4
56 5 0.1 0.1
57 6 0.2 0.2
58 12 0.3 0.3
59 5 0.1 0.1
60 22 0.6 0.6
61 2 0.1 0.1
62 11 0.3 0.3
63 3 0.1 0.1
64 7 0.2 0.2
65 15 0.4 0.4
66 5 0.1 0.1
67 5 0.1 0.1
68 7 0.2 0.2
69 6 0.2 0.2
70 9 0.3 0.3
71 2 0.1 0.1
72 3 0.1 0.1
73 3 0.1 0.1
74 3 0.1 0.1
76 1 0.0 0.0
77 1 0.0 0.0
78 2 0.1 0.1
82 1 0.0 0.0
84 1 0.0 0.0
94 1 0.0 0.0
97 1 0.0 0.0
“NA” 3299 92.7 92.7
Total 3557 100.0 100.0
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  levels(b4fa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4fa <- ordered(b4fa, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")  
High Cholesterol
n % val%
No 1502 42.2 45.1
Yes 1828 51.4 54.8
Scantron_Error 3 0.1 0.1
NA 224 6.3 NA
Total 3557 100.0 100.0
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
1 3 0.1 0.1
10 5 0.1 0.1
12 2 0.1 0.1
14 1 0.0 0.0
15 1 0.0 0.0
16 2 0.1 0.1
17 1 0.0 0.0
18 1 0.0 0.0
19 4 0.1 0.1
2 1 0.0 0.0
20 3 0.1 0.1
21 2 0.1 0.1
22 1 0.0 0.0
24 5 0.1 0.1
25 4 0.1 0.1
26 2 0.1 0.1
27 3 0.1 0.1
28 6 0.2 0.2
29 2 0.1 0.1
30 14 0.4 0.4
31 4 0.1 0.1
32 6 0.2 0.2
33 1 0.0 0.0
34 7 0.2 0.2
35 35 1.0 1.0
36 15 0.4 0.4
37 4 0.1 0.1
38 16 0.4 0.4
39 7 0.2 0.2
4 1 0.0 0.0
40 74 2.1 2.1
41 8 0.2 0.2
42 14 0.4 0.4
43 9 0.3 0.3
44 11 0.3 0.3
45 86 2.4 2.4
46 14 0.4 0.4
47 13 0.4 0.4
48 29 0.8 0.8
49 12 0.3 0.3
5 5 0.1 0.1
50 174 4.9 4.9
51 19 0.5 0.5
52 29 0.8 0.8
53 19 0.5 0.5
54 37 1.0 1.0
55 134 3.8 3.8
56 37 1.0 1.0
57 42 1.2 1.2
58 39 1.1 1.1
59 32 0.9 0.9
6 2 0.1 0.1
60 138 3.9 3.9
61 24 0.7 0.7
62 57 1.6 1.6
63 29 0.8 0.8
64 25 0.7 0.7
65 53 1.5 1.5
66 13 0.4 0.4
67 16 0.4 0.4
68 23 0.6 0.6
69 25 0.7 0.7
7 2 0.1 0.1
70 31 0.9 0.9
71 6 0.2 0.2
72 13 0.4 0.4
73 6 0.2 0.2
74 9 0.3 0.3
75 8 0.2 0.2
76 3 0.1 0.1
77 1 0.0 0.0
8 1 0.0 0.0
80 1 0.0 0.0
86 1 0.0 0.0
9 1 0.0 0.0
92 1 0.0 0.0
94 1 0.0 0.0
95 1 0.0 0.0
96 1 0.0 0.0
97 1 0.0 0.0
99 2 0.1 0.1
“NA” 2066 58.1 58.1
Total 3557 100.0 100.0
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  levels(b4ga) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ga <- ordered(b4ga, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD") 
Asthma, COPD
n % val%
No 2927 82.3 84.6
Yes 529 14.9 15.3
Scantron_Error 2 0.1 0.1
NA 99 2.8 NA
Total 3557 100.0 100.0
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
06 2 0.1 0.1
1 10 0.3 0.3
10 19 0.5 0.5
11 4 0.1 0.1
12 11 0.3 0.3
13 2 0.1 0.1
14 5 0.1 0.1
15 4 0.1 0.1
16 3 0.1 0.1
17 2 0.1 0.1
18 9 0.3 0.3
19 3 0.1 0.1
2 6 0.2 0.2
20 6 0.2 0.2
21 2 0.1 0.1
23 1 0.0 0.0
25 6 0.2 0.2
26 1 0.0 0.0
27 3 0.1 0.1
28 2 0.1 0.1
29 1 0.0 0.0
3 2 0.1 0.1
30 8 0.2 0.2
31 1 0.0 0.0
32 3 0.1 0.1
33 2 0.1 0.1
35 4 0.1 0.1
37 1 0.0 0.0
38 3 0.1 0.1
39 1 0.0 0.0
4 10 0.3 0.3
40 10 0.3 0.3
42 4 0.1 0.1
43 1 0.0 0.0
45 9 0.3 0.3
46 1 0.0 0.0
47 1 0.0 0.0
48 4 0.1 0.1
49 2 0.1 0.1
5 28 0.8 0.8
50 24 0.7 0.7
51 4 0.1 0.1
52 6 0.2 0.2
53 2 0.1 0.1
54 3 0.1 0.1
55 16 0.4 0.4
56 12 0.3 0.3
57 13 0.4 0.4
58 9 0.3 0.3
59 10 0.3 0.3
6 14 0.4 0.4
60 28 0.8 0.8
61 2 0.1 0.1
62 11 0.3 0.3
63 11 0.3 0.3
64 6 0.2 0.2
65 16 0.4 0.4
66 5 0.1 0.1
67 7 0.2 0.2
68 7 0.2 0.2
69 6 0.2 0.2
7 12 0.3 0.3
70 9 0.3 0.3
71 6 0.2 0.2
72 2 0.1 0.1
73 4 0.1 0.1
74 2 0.1 0.1
75 4 0.1 0.1
77 3 0.1 0.1
78 2 0.1 0.1
79 1 0.0 0.0
8 8 0.2 0.2
80 1 0.0 0.0
81 1 0.0 0.0
9 8 0.2 0.2
93 1 0.0 0.0
97 1 0.0 0.0
“NA” 3083 86.7 86.7
Total 3557 100.0 100.0
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  levels(b4ha) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ha <- ordered(b4ha, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 3142 88.3 91.4
Yes 294 8.3 8.6
Scantron_Error 1 0.0 0.0
NA 120 3.4 NA
Total 3557 100.0 100.0
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
0 46 1.3 1.3
10 2 0.1 0.1
13 2 0.1 0.1
14 3 0.1 0.1
15 3 0.1 0.1
16 4 0.1 0.1
17 1 0.0 0.0
18 4 0.1 0.1
19 3 0.1 0.1
2 1 0.0 0.0
20 4 0.1 0.1
21 1 0.0 0.0
22 7 0.2 0.2
23 3 0.1 0.1
24 4 0.1 0.1
25 9 0.3 0.3
27 3 0.1 0.1
28 4 0.1 0.1
3 1 0.0 0.0
30 12 0.3 0.3
32 3 0.1 0.1
34 3 0.1 0.1
35 17 0.5 0.5
36 4 0.1 0.1
38 1 0.0 0.0
39 3 0.1 0.1
40 14 0.4 0.4
42 3 0.1 0.1
44 2 0.1 0.1
45 15 0.4 0.4
46 2 0.1 0.1
47 3 0.1 0.1
48 6 0.2 0.2
49 1 0.0 0.0
50 14 0.4 0.4
52 2 0.1 0.1
53 1 0.0 0.0
54 1 0.0 0.0
55 5 0.1 0.1
56 2 0.1 0.1
57 4 0.1 0.1
58 4 0.1 0.1
59 3 0.1 0.1
60 11 0.3 0.3
61 1 0.0 0.0
62 9 0.3 0.3
63 5 0.1 0.1
64 3 0.1 0.1
65 7 0.2 0.2
66 3 0.1 0.1
67 5 0.1 0.1
68 3 0.1 0.1
69 2 0.1 0.1
7 1 0.0 0.0
70 2 0.1 0.1
71 1 0.0 0.0
72 3 0.1 0.1
73 1 0.0 0.0
74 1 0.0 0.0
76 1 0.0 0.0
8 1 0.0 0.0
80 1 0.0 0.0
9 2 0.1 0.1
94 1 0.0 0.0
“NA” 3263 91.7 91.7
Total 3557 100.0 100.0
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  levels(b4ia) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ia <- ordered(b4ia, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 3314 93.2 96.7
Yes 114 3.2 3.3
Scantron_Error 0 0.0 0.0
NA 129 3.6 NA
Total 3557 100.0 100.0
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
15 1 0.0 0.0
2 2 0.1 0.1
22 2 0.1 0.1
30 1 0.0 0.0
32 3 0.1 0.1
33 1 0.0 0.0
34 1 0.0 0.0
35 2 0.1 0.1
37 1 0.0 0.0
39 1 0.0 0.0
40 11 0.3 0.3
41 1 0.0 0.0
44 5 0.1 0.1
45 2 0.1 0.1
46 1 0.0 0.0
48 2 0.1 0.1
50 8 0.2 0.2
51 3 0.1 0.1
52 3 0.1 0.1
53 1 0.0 0.0
54 2 0.1 0.1
55 8 0.2 0.2
56 2 0.1 0.1
57 1 0.0 0.0
58 5 0.1 0.1
59 1 0.0 0.0
60 6 0.2 0.2
62 1 0.0 0.0
63 2 0.1 0.1
64 1 0.0 0.0
65 3 0.1 0.1
66 2 0.1 0.1
67 1 0.0 0.0
69 3 0.1 0.1
70 2 0.1 0.1
71 2 0.1 0.1
74 1 0.0 0.0
75 2 0.1 0.1
76 2 0.1 0.1
79 1 0.0 0.0
85 1 0.0 0.0
“NA” 3456 97.2 97.2
Total 3557 100.0 100.0
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  levels(b4ja) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ja <- ordered(b4ja, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 2383 67.0 68.6
Yes 1086 30.5 31.3
Scantron_Error 4 0.1 0.1
NA 84 2.4 NA
Total 3557 100.0 100.0
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
0 10 0.3 0.3
10 1 0.0 0.0
12 2 0.1 0.1
13 2 0.1 0.1
14 2 0.1 0.1
15 2 0.1 0.1
16 1 0.0 0.0
17 4 0.1 0.1
18 1 0.0 0.0
19 1 0.0 0.0
2 1 0.0 0.0
20 3 0.1 0.1
22 1 0.0 0.0
24 2 0.1 0.1
27 1 0.0 0.0
28 2 0.1 0.1
29 5 0.1 0.1
3 2 0.1 0.1
30 14 0.4 0.4
31 1 0.0 0.0
32 1 0.0 0.0
34 1 0.0 0.0
35 21 0.6 0.6
36 3 0.1 0.1
37 5 0.1 0.1
38 7 0.2 0.2
39 6 0.2 0.2
40 37 1.0 1.0
41 4 0.1 0.1
42 13 0.4 0.4
43 12 0.3 0.3
44 8 0.2 0.2
45 49 1.4 1.4
46 8 0.2 0.2
47 12 0.3 0.3
48 17 0.5 0.5
49 8 0.2 0.2
5 1 0.0 0.0
50 79 2.2 2.2
51 13 0.4 0.4
52 16 0.4 0.4
53 20 0.6 0.6
54 28 0.8 0.8
55 78 2.2 2.2
56 28 0.8 0.8
57 22 0.6 0.6
58 39 1.1 1.1
59 28 0.8 0.8
60 75 2.1 2.1
61 29 0.8 0.8
62 37 1.0 1.0
63 17 0.5 0.5
64 21 0.6 0.6
65 40 1.1 1.1
66 14 0.4 0.4
67 5 0.1 0.1
68 13 0.4 0.4
69 16 0.4 0.4
70 24 0.7 0.7
71 6 0.2 0.2
72 4 0.1 0.1
73 2 0.1 0.1
74 3 0.1 0.1
75 5 0.1 0.1
76 2 0.1 0.1
78 1 0.0 0.0
81 1 0.0 0.0
94 1 0.0 0.0
95 1 0.0 0.0
97 1 0.0 0.0
“NA” 2617 73.6 73.6
Total 3557 100.0 100.0
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  levels(b4ka) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ka <- ordered(b4ka, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 3253 91.5 94.0
Yes 206 5.8 6.0
Scantron_Error 2 0.1 0.1
NA 96 2.7 NA
Total 3557 100.0 100.0
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
11 1 0.0 0.0
15 3 0.1 0.1
16 1 0.0 0.0
17 1 0.0 0.0
29 1 0.0 0.0
35 2 0.1 0.1
38 1 0.0 0.0
40 5 0.1 0.1
42 1 0.0 0.0
44 1 0.0 0.0
45 3 0.1 0.1
46 1 0.0 0.0
47 1 0.0 0.0
48 1 0.0 0.0
49 1 0.0 0.0
5 2 0.1 0.1
50 11 0.3 0.3
51 3 0.1 0.1
52 2 0.1 0.1
53 4 0.1 0.1
54 5 0.1 0.1
55 11 0.3 0.3
56 6 0.2 0.2
57 4 0.1 0.1
58 4 0.1 0.1
59 5 0.1 0.1
60 6 0.2 0.2
61 5 0.1 0.1
62 8 0.2 0.2
63 1 0.0 0.0
64 5 0.1 0.1
65 11 0.3 0.3
66 4 0.1 0.1
67 6 0.2 0.2
68 6 0.2 0.2
69 5 0.1 0.1
7 1 0.0 0.0
70 7 0.2 0.2
71 3 0.1 0.1
72 3 0.1 0.1
74 2 0.1 0.1
75 4 0.1 0.1
76 1 0.0 0.0
77 2 0.1 0.1
80 1 0.0 0.0
83 1 0.0 0.0
95 1 0.0 0.0
“NA” 3393 95.4 95.4
Total 3557 100.0 100.0
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  levels(b4la) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4la <- ordered(b4la, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 3376 94.9 97.9
Yes 71 2.0 2.1
Scantron_Error 0 0.0 0.0
NA 110 3.1 NA
Total 3557 100.0 100.0
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
18 1 0.0 0.0
21 1 0.0 0.0
39 1 0.0 0.0
40 1 0.0 0.0
42 1 0.0 0.0
45 5 0.1 0.1
47 1 0.0 0.0
48 2 0.1 0.1
49 1 0.0 0.0
50 2 0.1 0.1
51 2 0.1 0.1
53 1 0.0 0.0
54 2 0.1 0.1
55 3 0.1 0.1
58 1 0.0 0.0
60 8 0.2 0.2
61 1 0.0 0.0
63 1 0.0 0.0
64 1 0.0 0.0
65 3 0.1 0.1
66 3 0.1 0.1
67 2 0.1 0.1
68 2 0.1 0.1
69 2 0.1 0.1
74 1 0.0 0.0
77 1 0.0 0.0
“NA” 3507 98.6 98.6
Total 3557 100.0 100.0
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  levels(b4ma) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ma <- ordered(b4ma, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 3006 84.5 87.3
Yes 436 12.3 12.7
Scantron_Error 2 0.1 0.1
NA 113 3.2 NA
Total 3557 100.0 100.0
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
1 1 0.0 0.0
10 1 0.0 0.0
17 1 0.0 0.0
19 1 0.0 0.0
2 1 0.0 0.0
20 2 0.1 0.1
21 1 0.0 0.0
22 1 0.0 0.0
23 1 0.0 0.0
24 1 0.0 0.0
25 3 0.1 0.1
28 1 0.0 0.0
30 9 0.3 0.3
32 2 0.1 0.1
33 1 0.0 0.0
35 7 0.2 0.2
36 2 0.1 0.1
38 2 0.1 0.1
39 3 0.1 0.1
4 2 0.1 0.1
40 19 0.5 0.5
41 5 0.1 0.1
42 4 0.1 0.1
43 4 0.1 0.1
44 4 0.1 0.1
45 17 0.5 0.5
46 2 0.1 0.1
47 4 0.1 0.1
48 7 0.2 0.2
49 2 0.1 0.1
50 37 1.0 1.0
51 7 0.2 0.2
52 8 0.2 0.2
53 6 0.2 0.2
54 9 0.3 0.3
55 32 0.9 0.9
56 7 0.2 0.2
57 7 0.2 0.2
58 14 0.4 0.4
59 2 0.1 0.1
60 34 1.0 1.0
61 5 0.1 0.1
62 10 0.3 0.3
63 8 0.2 0.2
64 9 0.3 0.3
65 16 0.4 0.4
66 2 0.1 0.1
67 6 0.2 0.2
68 7 0.2 0.2
69 5 0.1 0.1
7 1 0.0 0.0
70 6 0.2 0.2
71 3 0.1 0.1
72 5 0.1 0.1
73 1 0.0 0.0
74 1 0.0 0.0
75 3 0.1 0.1
76 1 0.0 0.0
77 1 0.0 0.0
80 1 0.0 0.0
“NA” 3192 89.7 89.7
Total 3557 100.0 100.0
# Dementia
  b4na <- as.factor(d[,"b4na"])
  levels(b4na) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4na <- ordered(b4na, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 3420 96.1 98.7
Yes 45 1.3 1.3
Scantron_Error 1 0.0 0.0
NA 91 2.6 NA
Total 3557 100.0 100.0
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
10 1 0.0 0.0
29 1 0.0 0.0
53 1 0.0 0.0
57 1 0.0 0.0
60 1 0.0 0.0
62 2 0.1 0.1
63 1 0.0 0.0
64 1 0.0 0.0
65 2 0.1 0.1
66 1 0.0 0.0
67 1 0.0 0.0
69 1 0.0 0.0
70 6 0.2 0.2
72 3 0.1 0.1
73 2 0.1 0.1
74 1 0.0 0.0
75 1 0.0 0.0
80 1 0.0 0.0
“NA” 3529 99.2 99.2
Total 3557 100.0 100.0
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  levels(b4oa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4oa <- ordered(b4oa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 2992 84.1 87.0
Yes 447 12.6 13.0
Scantron_Error 2 0.1 0.1
NA 116 3.3 NA
Total 3557 100.0 100.0
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
1 1 0.0 0.0
14 1 0.0 0.0
16 1 0.0 0.0
17 1 0.0 0.0
18 1 0.0 0.0
19 6 0.2 0.2
20 6 0.2 0.2
21 5 0.1 0.1
22 1 0.0 0.0
24 1 0.0 0.0
25 4 0.1 0.1
26 3 0.1 0.1
28 6 0.2 0.2
29 2 0.1 0.1
30 5 0.1 0.1
32 4 0.1 0.1
33 1 0.0 0.0
34 1 0.0 0.0
35 10 0.3 0.3
36 4 0.1 0.1
37 4 0.1 0.1
38 3 0.1 0.1
39 1 0.0 0.0
4 1 0.0 0.0
40 15 0.4 0.4
41 2 0.1 0.1
42 8 0.2 0.2
43 5 0.1 0.1
44 3 0.1 0.1
45 12 0.3 0.3
46 3 0.1 0.1
47 7 0.2 0.2
48 7 0.2 0.2
49 6 0.2 0.2
50 27 0.8 0.8
51 3 0.1 0.1
52 9 0.3 0.3
53 6 0.2 0.2
54 5 0.1 0.1
55 22 0.6 0.6
56 7 0.2 0.2
57 6 0.2 0.2
58 9 0.3 0.3
59 7 0.2 0.2
6 1 0.0 0.0
60 17 0.5 0.5
61 10 0.3 0.3
62 6 0.2 0.2
63 5 0.1 0.1
64 6 0.2 0.2
65 15 0.4 0.4
66 6 0.2 0.2
67 4 0.1 0.1
68 8 0.2 0.2
69 1 0.0 0.0
7 2 0.1 0.1
70 9 0.3 0.3
72 3 0.1 0.1
73 2 0.1 0.1
74 2 0.1 0.1
75 1 0.0 0.0
76 1 0.0 0.0
8 1 0.0 0.0
91 1 0.0 0.0
96 1 0.0 0.0
98 2 0.1 0.1
“NA” 3211 90.3 90.3
Total 3557 100.0 100.0
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  levels(b4pa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4pa <- ordered(b4pa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 3408 95.8 98.8
Yes 41 1.2 1.2
Scantron_Error 0 0.0 0.0
NA 108 3.0 NA
Total 3557 100.0 100.0
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
0 47 1.3 1.3
25 1 0.0 0.0
30 1 0.0 0.0
33 1 0.0 0.0
36 2 0.1 0.1
38 1 0.0 0.0
39 1 0.0 0.0
40 3 0.1 0.1
45 1 0.0 0.0
48 1 0.0 0.0
49 2 0.1 0.1
50 2 0.1 0.1
51 1 0.0 0.0
55 1 0.0 0.0
59 2 0.1 0.1
60 1 0.0 0.0
63 1 0.0 0.0
65 1 0.0 0.0
9 1 0.0 0.0
90 1 0.0 0.0
“NA” 3485 98.0 98.0
Total 3557 100.0 100.0
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  levels(b4qa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4qa <- ordered(b4qa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 3172 89.2 93.3
Yes 228 6.4 6.7
Scantron_Error 1 0.0 0.0
NA 156 4.4 NA
Total 3557 100.0 100.0
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
10 1 0.0 0.0
12 1 0.0 0.0
16 1 0.0 0.0
18 1 0.0 0.0
19 1 0.0 0.0
2 1 0.0 0.0
20 1 0.0 0.0
22 1 0.0 0.0
24 1 0.0 0.0
30 1 0.0 0.0
35 1 0.0 0.0
36 1 0.0 0.0
38 1 0.0 0.0
39 1 0.0 0.0
40 2 0.1 0.1
41 1 0.0 0.0
42 3 0.1 0.1
43 3 0.1 0.1
44 4 0.1 0.1
45 1 0.0 0.0
47 2 0.1 0.1
48 1 0.0 0.0
49 6 0.2 0.2
50 7 0.2 0.2
51 3 0.1 0.1
52 2 0.1 0.1
54 7 0.2 0.2
55 7 0.2 0.2
56 7 0.2 0.2
57 3 0.1 0.1
58 12 0.3 0.3
59 4 0.1 0.1
60 13 0.4 0.4
61 11 0.3 0.3
62 10 0.3 0.3
63 10 0.3 0.3
64 5 0.1 0.1
65 6 0.2 0.2
66 8 0.2 0.2
67 6 0.2 0.2
68 8 0.2 0.2
69 8 0.2 0.2
7 1 0.0 0.0
70 6 0.2 0.2
71 3 0.1 0.1
72 4 0.1 0.1
73 2 0.1 0.1
74 6 0.2 0.2
75 2 0.1 0.1
76 1 0.0 0.0
77 1 0.0 0.0
78 1 0.0 0.0
79 1 0.0 0.0
“NA” 3355 94.3 94.3
Total 3557 100.0 100.0

LA County

Heart Attack
n % val%
No 291 90.7 92.4
Yes 24 7.5 7.6
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
Heart Attack age
n % val%
0 0 0.0 0.0
1 0 0.0 0.0
14 0 0.0 0.0
17 0 0.0 0.0
20 0 0.0 0.0
24 0 0.0 0.0
25 1 0.3 0.3
26 0 0.0 0.0
27 0 0.0 0.0
29 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 1 0.3 0.3
40 1 0.3 0.3
42 1 0.3 0.3
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
5 0 0.0 0.0
50 3 0.9 0.9
51 1 0.3 0.3
52 2 0.6 0.6
53 0 0.0 0.0
54 2 0.6 0.6
55 2 0.6 0.6
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.3 0.3
59 0 0.0 0.0
60 2 0.6 0.6
61 1 0.3 0.3
62 0 0.0 0.0
63 1 0.3 0.3
64 1 0.3 0.3
65 1 0.3 0.3
66 1 0.3 0.3
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 1 0.3 0.3
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
79 0 0.0 0.0
93 0 0.0 0.0
“NA” 297 92.5 92.5
Total 321 100.0 100.0
Heart Failure or CHF
n % val%
No 294 91.6 93.3
Yes 21 6.5 6.7
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
Heart Failure or CHF age
n % val%
12 1 0.3 0.3
13 1 0.3 0.3
17 0 0.0 0.0
18 0 0.0 0.0
20 0 0.0 0.0
25 1 0.3 0.3
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 1 0.3 0.3
42 1 0.3 0.3
43 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 1 0.3 0.3
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 2 0.6 0.6
61 0 0.0 0.0
62 1 0.3 0.3
63 1 0.3 0.3
64 1 0.3 0.3
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.3 0.3
71 2 0.6 0.6
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
80 0 0.0 0.0
82 0 0.0 0.0
97 0 0.0 0.0
“NA” 302 94.1 94.1
Total 321 100.0 100.0
Stroke
n % val%
No 289 90.0 92.3
Yes 24 7.5 7.7
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
Stroke age
n % val%
0 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
30 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 1 0.3 0.3
45 2 0.6 0.6
46 1 0.3 0.3
47 1 0.3 0.3
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 1 0.3 0.3
51 0 0.0 0.0
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.3 0.3
58 1 0.3 0.3
59 0 0.0 0.0
60 1 0.3 0.3
61 2 0.6 0.6
62 0 0.0 0.0
63 3 0.9 0.9
64 1 0.3 0.3
65 1 0.3 0.3
66 2 0.6 0.6
67 1 0.3 0.3
68 1 0.3 0.3
69 2 0.6 0.6
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
“NA” 298 92.8 92.8
Total 321 100.0 100.0
Hypertension
n % val%
No 99 30.8 31.5
Yes 215 67.0 68.5
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
Hypertension age
n % val%
.35 0 0.0 0.0
1 0 0.0 0.0
10 1 0.3 0.3
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 1 0.3 0.3
19 0 0.0 0.0
2 1 0.3 0.3
20 0 0.0 0.0
21 0 0.0 0.0
22 1 0.3 0.3
23 0 0.0 0.0
24 1 0.3 0.3
25 2 0.6 0.6
26 0 0.0 0.0
27 1 0.3 0.3
28 0 0.0 0.0
29 1 0.3 0.3
30 5 1.6 1.6
31 0 0.0 0.0
32 6 1.9 1.9
33 0 0.0 0.0
34 1 0.3 0.3
35 8 2.5 2.5
36 0 0.0 0.0
37 3 0.9 0.9
38 6 1.9 1.9
39 2 0.6 0.6
4 0 0.0 0.0
40 12 3.7 3.7
41 3 0.9 0.9
42 7 2.2 2.2
43 2 0.6 0.6
44 0 0.0 0.0
45 20 6.2 6.2
46 3 0.9 0.9
47 1 0.3 0.3
48 6 1.9 1.9
49 2 0.6 0.6
5 0 0.0 0.0
50 16 5.0 5.0
51 2 0.6 0.6
52 8 2.5 2.5
53 1 0.3 0.3
54 0 0.0 0.0
55 15 4.7 4.7
56 7 2.2 2.2
57 3 0.9 0.9
58 8 2.5 2.5
59 1 0.3 0.3
6 0 0.0 0.0
60 11 3.4 3.4
61 4 1.2 1.2
62 6 1.9 1.9
63 2 0.6 0.6
64 0 0.0 0.0
65 9 2.8 2.8
66 0 0.0 0.0
67 0 0.0 0.0
68 4 1.2 1.2
69 1 0.3 0.3
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 2 0.6 0.6
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
89 0 0.0 0.0
9 1 0.3 0.3
92 0 0.0 0.0
94 1 0.3 0.3
96 0 0.0 0.0
98 1 0.3 0.3
99 0 0.0 0.0
“NA” 122 38.0 38.0
Total 321 100.0 100.0
Peripheral arterial disease
n % val%
No 301 93.8 96.8
Yes 10 3.1 3.2
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 0 0.0 0.0
1 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 1 0.3 0.3
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
5 0 0.0 0.0
50 0 0.0 0.0
51 1 0.3 0.3
52 0 0.0 0.0
53 0 0.0 0.0
54 1 0.3 0.3
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 3 0.9 0.9
59 0 0.0 0.0
60 1 0.3 0.3
61 0 0.0 0.0
62 2 0.6 0.6
63 0 0.0 0.0
64 2 0.6 0.6
65 3 0.9 0.9
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 1 0.3 0.3
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
82 0 0.0 0.0
84 0 0.0 0.0
94 0 0.0 0.0
97 0 0.0 0.0
“NA” 304 94.7 94.7
Total 321 100.0 100.0
High Cholesterol
n % val%
No 141 43.9 44.9
Yes 172 53.6 54.8
Scantron_Error 1 0.3 0.3
NA 7 2.2 NA
Total 321 100.0 100.0
High Cholesterol age
n % val%
1 0 0.0 0.0
10 1 0.3 0.3
12 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 1 0.3 0.3
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 1 0.3 0.3
33 0 0.0 0.0
34 1 0.3 0.3
35 4 1.2 1.2
36 1 0.3 0.3
37 0 0.0 0.0
38 1 0.3 0.3
39 2 0.6 0.6
4 0 0.0 0.0
40 6 1.9 1.9
41 1 0.3 0.3
42 5 1.6 1.6
43 2 0.6 0.6
44 1 0.3 0.3
45 8 2.5 2.5
46 0 0.0 0.0
47 0 0.0 0.0
48 4 1.2 1.2
49 0 0.0 0.0
5 1 0.3 0.3
50 18 5.6 5.6
51 2 0.6 0.6
52 7 2.2 2.2
53 0 0.0 0.0
54 1 0.3 0.3
55 16 5.0 5.0
56 1 0.3 0.3
57 2 0.6 0.6
58 14 4.4 4.4
59 2 0.6 0.6
6 0 0.0 0.0
60 14 4.4 4.4
61 1 0.3 0.3
62 6 1.9 1.9
63 3 0.9 0.9
64 4 1.2 1.2
65 2 0.6 0.6
66 0 0.0 0.0
67 1 0.3 0.3
68 3 0.9 0.9
69 4 1.2 1.2
7 0 0.0 0.0
70 1 0.3 0.3
71 1 0.3 0.3
72 2 0.6 0.6
73 1 0.3 0.3
74 2 0.6 0.6
75 1 0.3 0.3
76 0 0.0 0.0
77 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
86 0 0.0 0.0
9 1 0.3 0.3
92 0 0.0 0.0
94 1 0.3 0.3
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 0 0.0 0.0
“NA” 170 53.0 53.0
Total 321 100.0 100.0
Asthma, COPD
n % val%
No 270 84.1 84.9
Yes 48 15.0 15.1
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 1 0.3 0.3
10 3 0.9 0.9
11 0 0.0 0.0
12 1 0.3 0.3
13 0 0.0 0.0
14 1 0.3 0.3
15 1 0.3 0.3
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 1 0.3 0.3
21 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 1 0.3 0.3
33 0 0.0 0.0
35 1 0.3 0.3
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 3 0.9 0.9
40 1 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
5 4 1.2 1.2
50 1 0.3 0.3
51 0 0.0 0.0
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.3 0.3
59 1 0.3 0.3
6 3 0.9 0.9
60 4 1.2 1.2
61 0 0.0 0.0
62 0 0.0 0.0
63 2 0.6 0.6
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.3 0.3
69 1 0.3 0.3
7 2 0.6 0.6
70 2 0.6 0.6
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 1 0.3 0.3
79 0 0.0 0.0
8 4 1.2 1.2
80 0 0.0 0.0
81 0 0.0 0.0
9 0 0.0 0.0
93 0 0.0 0.0
97 0 0.0 0.0
“NA” 275 85.7 85.7
Total 321 100.0 100.0
Stomach ulcers
n % val%
No 290 90.3 91.5
Yes 27 8.4 8.5
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
Stomach ulcers age
n % val%
0 0 0.0 0.0
10 1 0.3 0.3
13 1 0.3 0.3
14 0 0.0 0.0
15 1 0.3 0.3
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 2 0.6 0.6
23 1 0.3 0.3
24 0 0.0 0.0
25 2 0.6 0.6
27 0 0.0 0.0
28 2 0.6 0.6
3 1 0.3 0.3
30 0 0.0 0.0
32 1 0.3 0.3
34 1 0.3 0.3
35 1 0.3 0.3
36 0 0.0 0.0
38 1 0.3 0.3
39 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
44 1 0.3 0.3
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 2 0.6 0.6
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 1 0.3 0.3
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
9 0 0.0 0.0
94 0 0.0 0.0
“NA” 297 92.5 92.5
Total 321 100.0 100.0
Crohn’s Disease
n % val%
No 304 94.7 96.2
Yes 12 3.7 3.8
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
Crohn’s Disease age
n % val%
15 0 0.0 0.0
2 0 0.0 0.0
22 1 0.3 0.3
30 0 0.0 0.0
32 2 0.6 0.6
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
37 0 0.0 0.0
39 0 0.0 0.0
40 2 0.6 0.6
41 1 0.3 0.3
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
48 0 0.0 0.0
50 2 0.6 0.6
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 2 0.6 0.6
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.3 0.3
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
79 0 0.0 0.0
85 0 0.0 0.0
“NA” 310 96.6 96.6
Total 321 100.0 100.0
Diabetes
n % val%
No 228 71.0 72.2
Yes 88 27.4 27.8
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
Diabetes age
n % val%
0 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 2 0.6 0.6
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 1 0.3 0.3
30 2 0.6 0.6
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 1 0.3 0.3
36 0 0.0 0.0
37 0 0.0 0.0
38 1 0.3 0.3
39 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 3 0.9 0.9
43 0 0.0 0.0
44 1 0.3 0.3
45 8 2.5 2.5
46 0 0.0 0.0
47 0 0.0 0.0
48 2 0.6 0.6
49 0 0.0 0.0
5 0 0.0 0.0
50 3 0.9 0.9
51 2 0.6 0.6
52 2 0.6 0.6
53 2 0.6 0.6
54 4 1.2 1.2
55 6 1.9 1.9
56 1 0.3 0.3
57 1 0.3 0.3
58 5 1.6 1.6
59 1 0.3 0.3
60 4 1.2 1.2
61 4 1.2 1.2
62 7 2.2 2.2
63 1 0.3 0.3
64 1 0.3 0.3
65 5 1.6 1.6
66 4 1.2 1.2
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.3 0.3
70 0 0.0 0.0
71 1 0.3 0.3
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.3 0.3
76 1 0.3 0.3
78 0 0.0 0.0
81 0 0.0 0.0
94 1 0.3 0.3
95 0 0.0 0.0
97 0 0.0 0.0
“NA” 241 75.1 75.1
Total 321 100.0 100.0
Kidney Problems
n % val%
No 302 94.1 95
Yes 16 5.0 5
Scantron_Error 0 0.0 0
NA 3 0.9 NA
Total 321 100.0 100
Kidney Problems age
n % val%
11 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
29 0 0.0 0.0
35 1 0.3 0.3
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 1 0.3 0.3
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 1 0.3 0.3
53 1 0.3 0.3
54 0 0.0 0.0
55 2 0.6 0.6
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 0.3 0.3
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 1 0.3 0.3
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.3 0.3
71 0 0.0 0.0
72 1 0.3 0.3
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 1 0.3 0.3
83 0 0.0 0.0
95 0 0.0 0.0
“NA” 306 95.3 95.3
Total 321 100.0 100.0
Cirrhosis, liver damage
n % val%
No 310 96.6 97.5
Yes 8 2.5 2.5
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 0 0.0 0.0
21 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
45 2 0.6 0.6
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 1 0.3 0.3
51 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
58 1 0.3 0.3
60 2 0.6 0.6
61 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.3 0.3
74 0 0.0 0.0
77 0 0.0 0.0
“NA” 314 97.8 97.8
Total 321 100.0 100.0
Arthritis
n % val%
No 268 83.5 84.8
Yes 47 14.6 14.9
Scantron_Error 1 0.3 0.3
NA 5 1.6 NA
Total 321 100.0 100.0
Arthritis age
n % val%
1 1 0.3 0.3
10 1 0.3 0.3
17 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
28 1 0.3 0.3
30 1 0.3 0.3
32 1 0.3 0.3
33 0 0.0 0.0
35 1 0.3 0.3
36 1 0.3 0.3
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 3 0.9 0.9
41 1 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 1 0.3 0.3
50 5 1.6 1.6
51 1 0.3 0.3
52 1 0.3 0.3
53 1 0.3 0.3
54 3 0.9 0.9
55 2 0.6 0.6
56 0 0.0 0.0
57 1 0.3 0.3
58 0 0.0 0.0
59 0 0.0 0.0
60 3 0.9 0.9
61 1 0.3 0.3
62 3 0.9 0.9
63 2 0.6 0.6
64 2 0.6 0.6
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 1 0.3 0.3
77 0 0.0 0.0
80 0 0.0 0.0
“NA” 280 87.2 87.2
Total 321 100.0 100.0
Dementia
n % val%
No 317 98.8 99.4
Yes 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
Dementia age
n % val%
10 0 0.0 0.0
29 0 0.0 0.0
53 0 0.0 0.0
57 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 1 0.3 0.3
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
80 0 0.0 0.0
“NA” 319 99.4 99.4
Total 321 100.0 100.0
Depression
n % val%
No 276 86.0 86.8
Yes 42 13.1 13.2
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 1 0.3 0.3
24 0 0.0 0.0
25 2 0.6 0.6
26 0 0.0 0.0
28 1 0.3 0.3
29 1 0.3 0.3
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 3 0.9 0.9
36 0 0.0 0.0
37 1 0.3 0.3
38 0 0.0 0.0
39 1 0.3 0.3
4 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 1 0.3 0.3
43 0 0.0 0.0
44 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 1 0.3 0.3
51 1 0.3 0.3
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 4 1.2 1.2
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 1 0.3 0.3
6 0 0.0 0.0
60 1 0.3 0.3
61 2 0.6 0.6
62 0 0.0 0.0
63 0 0.0 0.0
64 2 0.6 0.6
65 2 0.6 0.6
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
72 1 0.3 0.3
73 1 0.3 0.3
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
“NA” 292 91.0 91.0
Total 321 100.0 100.0
AIDS
n % val%
No 316 98.4 99.1
Yes 3 0.9 0.9
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
AIDS age
n % val%
0 0 0.0 0.0
25 0 0.0 0.0
30 0 0.0 0.0
33 0 0.0 0.0
36 1 0.3 0.3
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
45 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
55 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
63 0 0.0 0.0
65 1 0.3 0.3
9 0 0.0 0.0
90 0 0.0 0.0
“NA” 319 99.4 99.4
Total 321 100.0 100.0
Other Cancer
n % val%
No 296 92.2 94.9
Yes 16 5.0 5.1
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 0 0.0 0.0
16 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
30 0 0.0 0.0
35 1 0.3 0.3
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 1 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
44 1 0.3 0.3
45 0 0.0 0.0
47 1 0.3 0.3
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.3 0.3
59 0 0.0 0.0
60 2 0.6 0.6
61 0 0.0 0.0
62 2 0.6 0.6
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.3 0.3
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
7 1 0.3 0.3
70 0 0.0 0.0
71 0 0.0 0.0
72 1 0.3 0.3
73 1 0.3 0.3
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
“NA” 304 94.7 94.7
Total 321 100.0 100.0

Northern CA

Heart Attack
n % val%
No 189 90.0 96.4
Yes 7 3.3 3.6
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
Heart Attack age
n % val%
0 9 4.3 4.3
1 0 0.0 0.0
14 0 0.0 0.0
17 0 0.0 0.0
20 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
29 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 3 1.4 1.4
51 0 0.0 0.0
52 1 0.5 0.5
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 2 1.0 1.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
79 0 0.0 0.0
93 0 0.0 0.0
“NA” 195 92.9 92.9
Total 210 100.0 100.0
Heart Failure or CHF
n % val%
No 182 86.7 91.9
Yes 16 7.6 8.1
Scantron_Error 0 0.0 0.0
NA 12 5.7 NA
Total 210 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
20 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 1 0.5 0.5
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 1 0.5 0.5
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 1 0.5 0.5
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 1 0.5 0.5
60 2 1.0 1.0
61 0 0.0 0.0
62 1 0.5 0.5
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 1 0.5 0.5
68 1 0.5 0.5
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.5 0.5
77 1 0.5 0.5
78 1 0.5 0.5
80 0 0.0 0.0
82 0 0.0 0.0
97 0 0.0 0.0
“NA” 197 93.8 93.8
Total 210 100.0 100.0
Stroke
n % val%
No 186 88.6 94.9
Yes 10 4.8 5.1
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
Stroke age
n % val%
0 9 4.3 4.3
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
30 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 1 0.5 0.5
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 2 1.0 1.0
61 3 1.4 1.4
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.5 0.5
67 0 0.0 0.0
68 1 0.5 0.5
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
“NA” 192 91.4 91.4
Total 210 100.0 100.0
Hypertension
n % val%
No 66 31.4 33.5
Yes 131 62.4 66.5
Scantron_Error 0 0.0 0.0
NA 13 6.2 NA
Total 210 100.0 100.0
Hypertension age
n % val%
.35 0 0.0 0.0
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 1 0.5 0.5
20 1 0.5 0.5
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 1 0.5 0.5
26 0 0.0 0.0
27 0 0.0 0.0
28 1 0.5 0.5
29 1 0.5 0.5
30 2 1.0 1.0
31 1 0.5 0.5
32 1 0.5 0.5
33 0 0.0 0.0
34 0 0.0 0.0
35 3 1.4 1.4
36 0 0.0 0.0
37 1 0.5 0.5
38 1 0.5 0.5
39 0 0.0 0.0
4 0 0.0 0.0
40 12 5.7 5.7
41 1 0.5 0.5
42 0 0.0 0.0
43 3 1.4 1.4
44 1 0.5 0.5
45 5 2.4 2.4
46 1 0.5 0.5
47 3 1.4 1.4
48 3 1.4 1.4
49 1 0.5 0.5
5 0 0.0 0.0
50 18 8.6 8.6
51 1 0.5 0.5
52 1 0.5 0.5
53 0 0.0 0.0
54 1 0.5 0.5
55 10 4.8 4.8
56 1 0.5 0.5
57 1 0.5 0.5
58 4 1.9 1.9
59 3 1.4 1.4
6 0 0.0 0.0
60 14 6.7 6.7
61 4 1.9 1.9
62 3 1.4 1.4
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.5 0.5
66 1 0.5 0.5
67 1 0.5 0.5
68 1 0.5 0.5
69 1 0.5 0.5
7 1 0.5 0.5
70 1 0.5 0.5
71 0 0.0 0.0
72 1 0.5 0.5
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
89 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
99 0 0.0 0.0
“NA” 97 46.2 46.2
Total 210 100.0 100.0
Peripheral arterial disease
n % val%
No 189 90.0 96.9
Yes 5 2.4 2.6
Scantron_Error 1 0.5 0.5
NA 15 7.1 NA
Total 210 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 0 0.0 0.0
1 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
5 0 0.0 0.0
50 2 1.0 1.0
51 1 0.5 0.5
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 0.5 0.5
61 1 0.5 0.5
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
82 0 0.0 0.0
84 0 0.0 0.0
94 0 0.0 0.0
97 0 0.0 0.0
“NA” 204 97.1 97.1
Total 210 100.0 100.0
High Cholesterol
n % val%
No 113 53.8 60.4
Yes 74 35.2 39.6
Scantron_Error 0 0.0 0.0
NA 23 11.0 NA
Total 210 100.0 100.0
High Cholesterol age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 2 1.0 1.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 3 1.4 1.4
41 1 0.5 0.5
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 5 2.4 2.4
46 0 0.0 0.0
47 0 0.0 0.0
48 2 1.0 1.0
49 0 0.0 0.0
5 0 0.0 0.0
50 7 3.3 3.3
51 1 0.5 0.5
52 1 0.5 0.5
53 0 0.0 0.0
54 2 1.0 1.0
55 8 3.8 3.8
56 2 1.0 1.0
57 2 1.0 1.0
58 0 0.0 0.0
59 1 0.5 0.5
6 0 0.0 0.0
60 10 4.8 4.8
61 2 1.0 1.0
62 2 1.0 1.0
63 3 1.4 1.4
64 0 0.0 0.0
65 3 1.4 1.4
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.5 0.5
69 0 0.0 0.0
7 1 0.5 0.5
70 3 1.4 1.4
71 0 0.0 0.0
72 1 0.5 0.5
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
86 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 0 0.0 0.0
“NA” 147 70.0 70.0
Total 210 100.0 100.0
Asthma, COPD
n % val%
No 173 82.4 84
Yes 33 15.7 16
Scantron_Error 0 0.0 0
NA 4 1.9 NA
Total 210 100.0 100
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 0 0.0 0.0
10 2 1.0 1.0
11 0 0.0 0.0
12 0 0.0 0.0
13 1 0.5 0.5
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 1 0.5 0.5
20 1 0.5 0.5
21 0 0.0 0.0
23 0 0.0 0.0
25 1 0.5 0.5
26 0 0.0 0.0
27 1 0.5 0.5
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 1 0.5 0.5
31 0 0.0 0.0
32 0 0.0 0.0
33 1 0.5 0.5
35 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 2 1.0 1.0
40 1 0.5 0.5
42 0 0.0 0.0
43 0 0.0 0.0
45 1 0.5 0.5
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 2 1.0 1.0
50 3 1.4 1.4
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 1 0.5 0.5
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.5 0.5
58 0 0.0 0.0
59 1 0.5 0.5
6 0 0.0 0.0
60 2 1.0 1.0
61 0 0.0 0.0
62 1 0.5 0.5
63 0 0.0 0.0
64 0 0.0 0.0
65 2 1.0 1.0
66 0 0.0 0.0
67 1 0.5 0.5
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 1 0.5 0.5
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.5 0.5
77 1 0.5 0.5
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
81 0 0.0 0.0
9 1 0.5 0.5
93 0 0.0 0.0
97 0 0.0 0.0
“NA” 179 85.2 85.2
Total 210 100.0 100.0
Stomach ulcers
n % val%
No 196 93.3 96.6
Yes 7 3.3 3.4
Scantron_Error 0 0.0 0.0
NA 7 3.3 NA
Total 210 100.0 100.0
Stomach ulcers age
n % val%
0 9 4.3 4.3
10 0 0.0 0.0
13 1 0.5 0.5
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 1 0.5 0.5
44 0 0.0 0.0
45 1 0.5 0.5
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.5 0.5
59 0 0.0 0.0
60 1 0.5 0.5
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.5 0.5
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
9 0 0.0 0.0
94 0 0.0 0.0
“NA” 195 92.9 92.9
Total 210 100.0 100.0
Crohn’s Disease
n % val%
No 192 91.4 94.6
Yes 11 5.2 5.4
Scantron_Error 0 0.0 0.0
NA 7 3.3 NA
Total 210 100.0 100.0
Crohn’s Disease age
n % val%
15 0 0.0 0.0
2 0 0.0 0.0
22 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
37 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
44 1 0.5 0.5
45 1 0.5 0.5
46 0 0.0 0.0
48 1 0.5 0.5
50 2 1.0 1.0
51 1 0.5 0.5
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.5 0.5
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 1 0.5 0.5
71 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 1 0.5 0.5
79 0 0.0 0.0
85 0 0.0 0.0
“NA” 200 95.2 95.2
Total 210 100.0 100.0
Diabetes
n % val%
No 154 73.3 75.1
Yes 51 24.3 24.9
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
Diabetes age
n % val%
0 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 1 0.5 0.5
22 0 0.0 0.0
24 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 1 0.5 0.5
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 2 1.0 1.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 1 0.5 0.5
45 3 1.4 1.4
46 1 0.5 0.5
47 0 0.0 0.0
48 1 0.5 0.5
49 0 0.0 0.0
5 0 0.0 0.0
50 3 1.4 1.4
51 0 0.0 0.0
52 0 0.0 0.0
53 2 1.0 1.0
54 0 0.0 0.0
55 5 2.4 2.4
56 4 1.9 1.9
57 1 0.5 0.5
58 2 1.0 1.0
59 1 0.5 0.5
60 6 2.9 2.9
61 3 1.4 1.4
62 0 0.0 0.0
63 1 0.5 0.5
64 2 1.0 1.0
65 1 0.5 0.5
66 1 0.5 0.5
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.5 0.5
70 0 0.0 0.0
71 0 0.0 0.0
72 1 0.5 0.5
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.5 0.5
76 0 0.0 0.0
78 0 0.0 0.0
81 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
97 0 0.0 0.0
“NA” 165 78.6 78.6
Total 210 100.0 100.0
Kidney Problems
n % val%
No 191 91.0 93.2
Yes 14 6.7 6.8
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
Kidney Problems age
n % val%
11 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
29 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 2 1.0 1.0
59 0 0.0 0.0
60 1 0.5 0.5
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 2 1.0 1.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.5 0.5
71 0 0.0 0.0
72 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
83 1 0.5 0.5
95 0 0.0 0.0
“NA” 202 96.2 96.2
Total 210 100.0 100.0
Cirrhosis, liver damage
n % val%
No 196 93.3 96.1
Yes 8 3.8 3.9
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 0 0.0 0.0
21 1 0.5 0.5
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
53 0 0.0 0.0
54 1 0.5 0.5
55 0 0.0 0.0
58 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 2 1.0 1.0
69 0 0.0 0.0
74 0 0.0 0.0
77 0 0.0 0.0
“NA” 206 98.1 98.1
Total 210 100.0 100.0
Arthritis
n % val%
No 182 86.7 88.8
Yes 23 11.0 11.2
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
28 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 1 0.5 0.5
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 3 1.4 1.4
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 1 0.5 0.5
55 2 1.0 1.0
56 0 0.0 0.0
57 0 0.0 0.0
58 2 1.0 1.0
59 1 0.5 0.5
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 2 1.0 1.0
64 2 1.0 1.0
65 2 1.0 1.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.5 0.5
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.5 0.5
76 0 0.0 0.0
77 1 0.5 0.5
80 0 0.0 0.0
“NA” 191 91.0 91.0
Total 210 100.0 100.0
Dementia
n % val%
No 200 95.2 98
Yes 4 1.9 2
Scantron_Error 0 0.0 0
NA 6 2.9 NA
Total 210 100.0 100
Dementia age
n % val%
10 0 0.0 0.0
29 0 0.0 0.0
53 0 0.0 0.0
57 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 1 0.5 0.5
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
80 0 0.0 0.0
“NA” 209 99.5 99.5
Total 210 100.0 100.0
Depression
n % val%
No 178 84.8 88.6
Yes 22 10.5 10.9
Scantron_Error 1 0.5 0.5
NA 9 4.3 NA
Total 210 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.5 0.5
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
28 1 0.5 0.5
29 0 0.0 0.0
30 1 0.5 0.5
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 1 0.5 0.5
44 0 0.0 0.0
45 1 0.5 0.5
46 0 0.0 0.0
47 0 0.0 0.0
48 2 1.0 1.0
49 0 0.0 0.0
50 2 1.0 1.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
6 1 0.5 0.5
60 2 1.0 1.0
61 0 0.0 0.0
62 0 0.0 0.0
63 1 0.5 0.5
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 1 0.5 0.5
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.5 0.5
72 1 0.5 0.5
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
“NA” 193 91.9 91.9
Total 210 100.0 100.0
AIDS
n % val%
No 196 93.3 96.1
Yes 8 3.8 3.9
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
AIDS age
n % val%
0 9 4.3 4.3
25 0 0.0 0.0
30 0 0.0 0.0
33 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 1 0.5 0.5
45 0 0.0 0.0
48 1 0.5 0.5
49 1 0.5 0.5
50 0 0.0 0.0
51 0 0.0 0.0
55 1 0.5 0.5
59 0 0.0 0.0
60 0 0.0 0.0
63 0 0.0 0.0
65 0 0.0 0.0
9 0 0.0 0.0
90 0 0.0 0.0
“NA” 197 93.8 93.8
Total 210 100.0 100.0
Other Cancer
n % val%
No 179 85.2 92.7
Yes 13 6.2 6.7
Scantron_Error 1 0.5 0.5
NA 17 8.1 NA
Total 210 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 0 0.0 0.0
16 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
30 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 1 0.5 0.5
44 0 0.0 0.0
45 0 0.0 0.0
47 0 0.0 0.0
48 1 0.5 0.5
49 1 0.5 0.5
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
54 0 0.0 0.0
55 1 0.5 0.5
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.5 0.5
59 0 0.0 0.0
60 0 0.0 0.0
61 1 0.5 0.5
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.5 0.5
67 2 1.0 1.0
68 0 0.0 0.0
69 2 1.0 1.0
7 0 0.0 0.0
70 0 0.0 0.0
71 1 0.5 0.5
72 1 0.5 0.5
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
“NA” 197 93.8 93.8
Total 210 100.0 100.0

Greater CA

Heart Attack
n % val%
No 277 87.9 92.6
Yes 22 7.0 7.4
Scantron_Error 0 0.0 0.0
NA 16 5.1 NA
Total 315 100.0 100.0
Heart Attack age
n % val%
0 4 1.3 1.3
1 0 0.0 0.0
14 0 0.0 0.0
17 0 0.0 0.0
20 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
29 1 0.3 0.3
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 2 0.6 0.6
49 1 0.3 0.3
5 0 0.0 0.0
50 2 0.6 0.6
51 0 0.0 0.0
52 2 0.6 0.6
53 1 0.3 0.3
54 0 0.0 0.0
55 2 0.6 0.6
56 0 0.0 0.0
57 1 0.3 0.3
58 3 1.0 1.0
59 2 0.6 0.6
60 0 0.0 0.0
61 1 0.3 0.3
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 2 0.6 0.6
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 2 0.6 0.6
76 0 0.0 0.0
79 0 0.0 0.0
93 0 0.0 0.0
“NA” 289 91.7 91.7
Total 315 100.0 100.0
Heart Failure or CHF
n % val%
No 274 87.0 93.2
Yes 20 6.3 6.8
Scantron_Error 0 0.0 0.0
NA 21 6.7 NA
Total 315 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
20 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 1 0.3 0.3
30 0 0.0 0.0
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 1 0.3 0.3
5 0 0.0 0.0
50 2 0.6 0.6
51 0 0.0 0.0
52 0 0.0 0.0
53 1 0.3 0.3
54 1 0.3 0.3
55 0 0.0 0.0
56 3 1.0 1.0
57 0 0.0 0.0
58 0 0.0 0.0
59 1 0.3 0.3
60 0 0.0 0.0
61 2 0.6 0.6
62 1 0.3 0.3
63 1 0.3 0.3
64 2 0.6 0.6
65 0 0.0 0.0
66 1 0.3 0.3
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 2 0.6 0.6
71 1 0.3 0.3
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.3 0.3
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
80 0 0.0 0.0
82 0 0.0 0.0
97 0 0.0 0.0
“NA” 293 93.0 93.0
Total 315 100.0 100.0
Stroke
n % val%
No 271 86.0 91.6
Yes 25 7.9 8.4
Scantron_Error 0 0.0 0.0
NA 19 6.0 NA
Total 315 100.0 100.0
Stroke age
n % val%
0 5 1.6 1.6
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
30 0 0.0 0.0
34 1 0.3 0.3
35 0 0.0 0.0
38 0 0.0 0.0
40 1 0.3 0.3
42 1 0.3 0.3
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 1 0.3 0.3
51 0 0.0 0.0
52 1 0.3 0.3
53 0 0.0 0.0
54 1 0.3 0.3
55 1 0.3 0.3
56 0 0.0 0.0
57 2 0.6 0.6
58 1 0.3 0.3
59 0 0.0 0.0
60 1 0.3 0.3
61 2 0.6 0.6
62 1 0.3 0.3
63 2 0.6 0.6
64 2 0.6 0.6
65 2 0.6 0.6
66 0 0.0 0.0
67 1 0.3 0.3
68 1 0.3 0.3
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 1 0.3 0.3
74 1 0.3 0.3
75 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
“NA” 286 90.8 90.8
Total 315 100.0 100.0
Hypertension
n % val%
No 89 28.3 29.6
Yes 212 67.3 70.4
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
Hypertension age
n % val%
.35 1 0.3 0.3
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 2 0.6 0.6
22 2 0.6 0.6
23 0 0.0 0.0
24 0 0.0 0.0
25 1 0.3 0.3
26 0 0.0 0.0
27 1 0.3 0.3
28 1 0.3 0.3
29 2 0.6 0.6
30 5 1.6 1.6
31 0 0.0 0.0
32 1 0.3 0.3
33 2 0.6 0.6
34 0 0.0 0.0
35 5 1.6 1.6
36 4 1.3 1.3
37 0 0.0 0.0
38 1 0.3 0.3
39 1 0.3 0.3
4 0 0.0 0.0
40 12 3.8 3.8
41 1 0.3 0.3
42 0 0.0 0.0
43 1 0.3 0.3
44 1 0.3 0.3
45 16 5.1 5.1
46 3 1.0 1.0
47 2 0.6 0.6
48 3 1.0 1.0
49 3 1.0 1.0
5 0 0.0 0.0
50 28 8.9 8.9
51 0 0.0 0.0
52 1 0.3 0.3
53 2 0.6 0.6
54 10 3.2 3.2
55 30 9.5 9.5
56 3 1.0 1.0
57 3 1.0 1.0
58 4 1.3 1.3
59 3 1.0 1.0
6 1 0.3 0.3
60 11 3.5 3.5
61 6 1.9 1.9
62 6 1.9 1.9
63 2 0.6 0.6
64 2 0.6 0.6
65 2 0.6 0.6
66 3 1.0 1.0
67 0 0.0 0.0
68 3 1.0 1.0
69 2 0.6 0.6
7 0 0.0 0.0
70 1 0.3 0.3
71 1 0.3 0.3
72 1 0.3 0.3
73 1 0.3 0.3
74 0 0.0 0.0
75 1 0.3 0.3
77 0 0.0 0.0
78 1 0.3 0.3
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
89 1 0.3 0.3
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
99 2 0.6 0.6
“NA” 113 35.9 35.9
Total 315 100.0 100.0
Peripheral arterial disease
n % val%
No 266 84.4 92.7
Yes 21 6.7 7.3
Scantron_Error 0 0.0 0.0
NA 28 8.9 NA
Total 315 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 1 0.3 0.3
1 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
25 1 0.3 0.3
26 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 1 0.3 0.3
37 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 2 0.6 0.6
46 1 0.3 0.3
47 0 0.0 0.0
48 3 1.0 1.0
5 0 0.0 0.0
50 2 0.6 0.6
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 1 0.3 0.3
57 0 0.0 0.0
58 1 0.3 0.3
59 0 0.0 0.0
60 1 0.3 0.3
61 0 0.0 0.0
62 1 0.3 0.3
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.3 0.3
67 0 0.0 0.0
68 3 1.0 1.0
69 1 0.3 0.3
70 1 0.3 0.3
71 1 0.3 0.3
72 0 0.0 0.0
73 1 0.3 0.3
74 1 0.3 0.3
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
82 0 0.0 0.0
84 0 0.0 0.0
94 0 0.0 0.0
97 0 0.0 0.0
“NA” 290 92.1 92.1
Total 315 100.0 100.0
High Cholesterol
n % val%
No 141 44.8 47.5
Yes 156 49.5 52.5
Scantron_Error 0 0.0 0.0
NA 18 5.7 NA
Total 315 100.0 100.0
High Cholesterol age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
12 1 0.3 0.3
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 1 0.3 0.3
27 0 0.0 0.0
28 1 0.3 0.3
29 1 0.3 0.3
30 1 0.3 0.3
31 0 0.0 0.0
32 0 0.0 0.0
33 1 0.3 0.3
34 0 0.0 0.0
35 2 0.6 0.6
36 3 1.0 1.0
37 0 0.0 0.0
38 1 0.3 0.3
39 1 0.3 0.3
4 0 0.0 0.0
40 9 2.9 2.9
41 0 0.0 0.0
42 2 0.6 0.6
43 0 0.0 0.0
44 2 0.6 0.6
45 13 4.1 4.1
46 2 0.6 0.6
47 4 1.3 1.3
48 3 1.0 1.0
49 1 0.3 0.3
5 1 0.3 0.3
50 19 6.0 6.0
51 1 0.3 0.3
52 0 0.0 0.0
53 4 1.3 1.3
54 6 1.9 1.9
55 13 4.1 4.1
56 3 1.0 1.0
57 2 0.6 0.6
58 3 1.0 1.0
59 2 0.6 0.6
6 1 0.3 0.3
60 11 3.5 3.5
61 2 0.6 0.6
62 3 1.0 1.0
63 1 0.3 0.3
64 3 1.0 1.0
65 5 1.6 1.6
66 0 0.0 0.0
67 2 0.6 0.6
68 2 0.6 0.6
69 2 0.6 0.6
7 0 0.0 0.0
70 2 0.6 0.6
71 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 1 0.3 0.3
76 0 0.0 0.0
77 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
86 0 0.0 0.0
9 0 0.0 0.0
92 1 0.3 0.3
94 0 0.0 0.0
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 0 0.0 0.0
“NA” 174 55.2 55.2
Total 315 100.0 100.0
Asthma, COPD
n % val%
No 254 80.6 83
Yes 52 16.5 17
Scantron_Error 0 0.0 0
NA 9 2.9 NA
Total 315 100.0 100
Asthma, COPD age
n % val%
06 2 0.6 0.6
1 0 0.0 0.0
10 0 0.0 0.0
11 0 0.0 0.0
12 1 0.3 0.3
13 0 0.0 0.0
14 1 0.3 0.3
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
23 0 0.0 0.0
25 1 0.3 0.3
26 0 0.0 0.0
27 1 0.3 0.3
28 0 0.0 0.0
29 0 0.0 0.0
3 1 0.3 0.3
30 2 0.6 0.6
31 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 6 1.9 1.9
50 5 1.6 1.6
51 0 0.0 0.0
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 3 1.0 1.0
56 1 0.3 0.3
57 0 0.0 0.0
58 2 0.6 0.6
59 0 0.0 0.0
6 2 0.6 0.6
60 2 0.6 0.6
61 0 0.0 0.0
62 1 0.3 0.3
63 1 0.3 0.3
64 2 0.6 0.6
65 1 0.3 0.3
66 0 0.0 0.0
67 1 0.3 0.3
68 0 0.0 0.0
69 1 0.3 0.3
7 5 1.6 1.6
70 1 0.3 0.3
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 1 0.3 0.3
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
81 0 0.0 0.0
9 1 0.3 0.3
93 0 0.0 0.0
97 1 0.3 0.3
“NA” 266 84.4 84.4
Total 315 100.0 100.0
Stomach ulcers
n % val%
No 283 89.8 94
Yes 18 5.7 6
Scantron_Error 0 0.0 0
NA 14 4.4 NA
Total 315 100.0 100
Stomach ulcers age
n % val%
0 5 1.6 1.6
10 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 1 0.3 0.3
16 0 0.0 0.0
17 0 0.0 0.0
18 1 0.3 0.3
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 1 0.3 0.3
22 0 0.0 0.0
23 0 0.0 0.0
24 1 0.3 0.3
25 1 0.3 0.3
27 0 0.0 0.0
28 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 3 1.0 1.0
36 1 0.3 0.3
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 2 0.6 0.6
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 2 0.6 0.6
66 1 0.3 0.3
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
9 0 0.0 0.0
94 0 0.0 0.0
“NA” 294 93.3 93.3
Total 315 100.0 100.0
Crohn’s Disease
n % val%
No 294 93.3 97.7
Yes 7 2.2 2.3
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
Crohn’s Disease age
n % val%
15 1 0.3 0.3
2 0 0.0 0.0
22 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 1 0.3 0.3
37 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
48 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.3 0.3
59 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 1 0.3 0.3
74 0 0.0 0.0
75 1 0.3 0.3
76 0 0.0 0.0
79 0 0.0 0.0
85 0 0.0 0.0
“NA” 310 98.4 98.4
Total 315 100.0 100.0
Diabetes
n % val%
No 214 67.9 69.9
Yes 92 29.2 30.1
Scantron_Error 0 0.0 0.0
NA 9 2.9 NA
Total 315 100.0 100.0
Diabetes age
n % val%
0 2 0.6 0.6
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 1 0.3 0.3
3 1 0.3 0.3
30 1 0.3 0.3
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 1 0.3 0.3
38 1 0.3 0.3
39 1 0.3 0.3
40 4 1.3 1.3
41 1 0.3 0.3
42 1 0.3 0.3
43 0 0.0 0.0
44 0 0.0 0.0
45 2 0.6 0.6
46 0 0.0 0.0
47 2 0.6 0.6
48 2 0.6 0.6
49 0 0.0 0.0
5 0 0.0 0.0
50 8 2.5 2.5
51 3 1.0 1.0
52 2 0.6 0.6
53 1 0.3 0.3
54 2 0.6 0.6
55 8 2.5 2.5
56 3 1.0 1.0
57 1 0.3 0.3
58 3 1.0 1.0
59 3 1.0 1.0
60 8 2.5 2.5
61 1 0.3 0.3
62 2 0.6 0.6
63 1 0.3 0.3
64 2 0.6 0.6
65 7 2.2 2.2
66 0 0.0 0.0
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
70 3 1.0 1.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 1 0.3 0.3
78 0 0.0 0.0
81 0 0.0 0.0
94 0 0.0 0.0
95 1 0.3 0.3
97 0 0.0 0.0
“NA” 233 74.0 74.0
Total 315 100.0 100.0
Kidney Problems
n % val%
No 285 90.5 94.4
Yes 16 5.1 5.3
Scantron_Error 1 0.3 0.3
NA 13 4.1 NA
Total 315 100.0 100.0
Kidney Problems age
n % val%
11 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
29 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 3 1.0 1.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.3 0.3
58 0 0.0 0.0
59 1 0.3 0.3
60 1 0.3 0.3
61 0 0.0 0.0
62 1 0.3 0.3
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 2 0.6 0.6
72 0 0.0 0.0
74 1 0.3 0.3
75 0 0.0 0.0
76 0 0.0 0.0
77 1 0.3 0.3
80 0 0.0 0.0
83 0 0.0 0.0
95 0 0.0 0.0
“NA” 302 95.9 95.9
Total 315 100.0 100.0
Cirrhosis, liver damage
n % val%
No 294 93.3 97
Yes 9 2.9 3
Scantron_Error 0 0.0 0
NA 12 3.8 NA
Total 315 100.0 100
Cirrhosis, liver damage age
n % val%
18 0 0.0 0.0
21 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 1 0.3 0.3
51 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
58 0 0.0 0.0
60 2 0.6 0.6
61 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 1 0.3 0.3
68 0 0.0 0.0
69 0 0.0 0.0
74 0 0.0 0.0
77 0 0.0 0.0
“NA” 311 98.7 98.7
Total 315 100.0 100.0
Arthritis
n % val%
No 277 87.9 92.6
Yes 21 6.7 7.0
Scantron_Error 1 0.3 0.3
NA 16 5.1 NA
Total 315 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
28 0 0.0 0.0
30 1 0.3 0.3
32 1 0.3 0.3
33 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 2 0.6 0.6
41 0 0.0 0.0
42 1 0.3 0.3
43 0 0.0 0.0
44 0 0.0 0.0
45 4 1.3 1.3
46 0 0.0 0.0
47 0 0.0 0.0
48 2 0.6 0.6
49 0 0.0 0.0
50 1 0.3 0.3
51 1 0.3 0.3
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 1 0.3 0.3
57 1 0.3 0.3
58 0 0.0 0.0
59 0 0.0 0.0
60 3 1.0 1.0
61 0 0.0 0.0
62 1 0.3 0.3
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
“NA” 294 93.3 93.3
Total 315 100.0 100.0
Dementia
n % val%
No 301 95.6 99
Yes 3 1.0 1
Scantron_Error 0 0.0 0
NA 11 3.5 NA
Total 315 100.0 100
Dementia age
n % val%
10 0 0.0 0.0
29 1 0.3 0.3
53 0 0.0 0.0
57 1 0.3 0.3
60 1 0.3 0.3
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
80 0 0.0 0.0
“NA” 311 98.7 98.7
Total 315 100.0 100.0
Depression
n % val%
No 271 86.0 89.7
Yes 31 9.8 10.3
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 1 0.3 0.3
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 2 0.6 0.6
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 1 0.3 0.3
32 0 0.0 0.0
33 0 0.0 0.0
34 1 0.3 0.3
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 1 0.3 0.3
39 0 0.0 0.0
4 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 2 0.6 0.6
43 0 0.0 0.0
44 1 0.3 0.3
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 1 0.3 0.3
50 0 0.0 0.0
51 0 0.0 0.0
52 1 0.3 0.3
53 1 0.3 0.3
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.3 0.3
58 0 0.0 0.0
59 0 0.0 0.0
6 0 0.0 0.0
60 1 0.3 0.3
61 1 0.3 0.3
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.3 0.3
66 2 0.6 0.6
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 3 1.0 1.0
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.3 0.3
75 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
“NA” 290 92.1 92.1
Total 315 100.0 100.0
AIDS
n % val%
No 300 95.2 98.7
Yes 4 1.3 1.3
Scantron_Error 0 0.0 0.0
NA 11 3.5 NA
Total 315 100.0 100.0
AIDS age
n % val%
0 5 1.6 1.6
25 0 0.0 0.0
30 1 0.3 0.3
33 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
45 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
55 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
63 1 0.3 0.3
65 0 0.0 0.0
9 1 0.3 0.3
90 0 0.0 0.0
“NA” 307 97.5 97.5
Total 315 100.0 100.0
Other Cancer
n % val%
No 277 87.9 93.6
Yes 19 6.0 6.4
Scantron_Error 0 0.0 0.0
NA 19 6.0 NA
Total 315 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 0 0.0 0.0
16 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
30 0 0.0 0.0
35 0 0.0 0.0
36 1 0.3 0.3
38 0 0.0 0.0
39 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 4 1.3 1.3
51 0 0.0 0.0
52 0 0.0 0.0
54 2 0.6 0.6
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 0.3 0.3
61 2 0.6 0.6
62 0 0.0 0.0
63 3 1.0 1.0
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 1 0.3 0.3
68 0 0.0 0.0
69 1 0.3 0.3
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
“NA” 298 94.6 94.6
Total 315 100.0 100.0

Detroit

Heart Attack
n % val%
No 313 87.9 91.5
Yes 29 8.1 8.5
Scantron_Error 0 0.0 0.0
NA 14 3.9 NA
Total 356 100.0 100.0
Heart Attack age
n % val%
0 8 2.2 2.2
1 0 0.0 0.0
14 0 0.0 0.0
17 1 0.3 0.3
20 1 0.3 0.3
24 0 0.0 0.0
25 0 0.0 0.0
26 1 0.3 0.3
27 0 0.0 0.0
29 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
44 0 0.0 0.0
45 3 0.8 0.8
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
5 0 0.0 0.0
50 1 0.3 0.3
51 1 0.3 0.3
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 1 0.3 0.3
57 0 0.0 0.0
58 2 0.6 0.6
59 1 0.3 0.3
60 3 0.8 0.8
61 1 0.3 0.3
62 0 0.0 0.0
63 2 0.6 0.6
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 3 0.8 0.8
68 0 0.0 0.0
69 0 0.0 0.0
70 1 0.3 0.3
71 0 0.0 0.0
72 0 0.0 0.0
73 1 0.3 0.3
74 0 0.0 0.0
76 1 0.3 0.3
79 1 0.3 0.3
93 0 0.0 0.0
“NA” 319 89.6 89.6
Total 356 100.0 100.0
Heart Failure or CHF
n % val%
No 317 89.0 93.5
Yes 22 6.2 6.5
Scantron_Error 0 0.0 0.0
NA 17 4.8 NA
Total 356 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 1 0.3 0.3
18 1 0.3 0.3
20 1 0.3 0.3
25 0 0.0 0.0
27 0 0.0 0.0
28 1 0.3 0.3
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 1 0.3 0.3
48 1 0.3 0.3
49 0 0.0 0.0
5 0 0.0 0.0
50 1 0.3 0.3
51 1 0.3 0.3
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 2 0.6 0.6
57 1 0.3 0.3
58 1 0.3 0.3
59 0 0.0 0.0
60 2 0.6 0.6
61 0 0.0 0.0
62 3 0.8 0.8
63 1 0.3 0.3
64 0 0.0 0.0
65 0 0.0 0.0
66 1 0.3 0.3
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 1 0.3 0.3
72 1 0.3 0.3
73 1 0.3 0.3
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
80 0 0.0 0.0
82 0 0.0 0.0
97 0 0.0 0.0
“NA” 334 93.8 93.8
Total 356 100.0 100.0
Stroke
n % val%
No 303 85.1 89.1
Yes 37 10.4 10.9
Scantron_Error 0 0.0 0.0
NA 16 4.5 NA
Total 356 100.0 100.0
Stroke age
n % val%
0 11 3.1 3.1
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
23 0 0.0 0.0
25 1 0.3 0.3
27 1 0.3 0.3
30 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 2 0.6 0.6
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 1 0.3 0.3
48 3 0.8 0.8
49 0 0.0 0.0
5 1 0.3 0.3
50 1 0.3 0.3
51 0 0.0 0.0
52 2 0.6 0.6
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.3 0.3
58 1 0.3 0.3
59 4 1.1 1.1
60 0 0.0 0.0
61 0 0.0 0.0
62 3 0.8 0.8
63 2 0.6 0.6
64 1 0.3 0.3
65 2 0.6 0.6
66 1 0.3 0.3
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
70 1 0.3 0.3
71 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
78 0 0.0 0.0
79 2 0.6 0.6
8 0 0.0 0.0
“NA” 312 87.6 87.6
Total 356 100.0 100.0
Hypertension
n % val%
No 101 28.4 30
Yes 236 66.3 70
Scantron_Error 0 0.0 0
NA 19 5.3 NA
Total 356 100.0 100
Hypertension age
n % val%
.35 0 0.0 0.0
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 1 0.3 0.3
17 1 0.3 0.3
18 1 0.3 0.3
19 0 0.0 0.0
2 0 0.0 0.0
20 2 0.6 0.6
21 1 0.3 0.3
22 1 0.3 0.3
23 1 0.3 0.3
24 3 0.8 0.8
25 0 0.0 0.0
26 2 0.6 0.6
27 0 0.0 0.0
28 1 0.3 0.3
29 1 0.3 0.3
30 7 2.0 2.0
31 1 0.3 0.3
32 1 0.3 0.3
33 1 0.3 0.3
34 0 0.0 0.0
35 10 2.8 2.8
36 0 0.0 0.0
37 2 0.6 0.6
38 1 0.3 0.3
39 1 0.3 0.3
4 1 0.3 0.3
40 19 5.3 5.3
41 1 0.3 0.3
42 3 0.8 0.8
43 3 0.8 0.8
44 1 0.3 0.3
45 14 3.9 3.9
46 1 0.3 0.3
47 3 0.8 0.8
48 3 0.8 0.8
49 4 1.1 1.1
5 2 0.6 0.6
50 28 7.9 7.9
51 2 0.6 0.6
52 3 0.8 0.8
53 2 0.6 0.6
54 7 2.0 2.0
55 13 3.7 3.7
56 4 1.1 1.1
57 3 0.8 0.8
58 6 1.7 1.7
59 3 0.8 0.8
6 0 0.0 0.0
60 16 4.5 4.5
61 2 0.6 0.6
62 4 1.1 1.1
63 5 1.4 1.4
64 3 0.8 0.8
65 2 0.6 0.6
66 0 0.0 0.0
67 3 0.8 0.8
68 2 0.6 0.6
69 3 0.8 0.8
7 0 0.0 0.0
70 2 0.6 0.6
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 2 0.6 0.6
75 1 0.3 0.3
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 2 0.6 0.6
89 0 0.0 0.0
9 1 0.3 0.3
92 0 0.0 0.0
94 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
99 0 0.0 0.0
“NA” 143 40.2 40.2
Total 356 100.0 100.0
Peripheral arterial disease
n % val%
No 298 83.7 91.1
Yes 29 8.1 8.9
Scantron_Error 0 0.0 0.0
NA 29 8.1 NA
Total 356 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 3 0.8 0.8
1 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
30 1 0.3 0.3
31 1 0.3 0.3
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 2 0.6 0.6
5 0 0.0 0.0
50 1 0.3 0.3
51 1 0.3 0.3
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 1 0.3 0.3
57 2 0.6 0.6
58 2 0.6 0.6
59 0 0.0 0.0
60 3 0.8 0.8
61 0 0.0 0.0
62 1 0.3 0.3
63 0 0.0 0.0
64 1 0.3 0.3
65 1 0.3 0.3
66 2 0.6 0.6
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.3 0.3
70 3 0.8 0.8
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 1 0.3 0.3
77 0 0.0 0.0
78 0 0.0 0.0
82 1 0.3 0.3
84 0 0.0 0.0
94 0 0.0 0.0
97 0 0.0 0.0
“NA” 327 91.9 91.9
Total 356 100.0 100.0
High Cholesterol
n % val%
No 160 44.9 46.9
Yes 181 50.8 53.1
Scantron_Error 0 0.0 0.0
NA 15 4.2 NA
Total 356 100.0 100.0
High Cholesterol age
n % val%
1 1 0.3 0.3
10 1 0.3 0.3
12 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 2 0.6 0.6
21 0 0.0 0.0
22 0 0.0 0.0
24 1 0.3 0.3
25 0 0.0 0.0
26 1 0.3 0.3
27 0 0.0 0.0
28 1 0.3 0.3
29 1 0.3 0.3
30 3 0.8 0.8
31 0 0.0 0.0
32 1 0.3 0.3
33 0 0.0 0.0
34 1 0.3 0.3
35 4 1.1 1.1
36 0 0.0 0.0
37 1 0.3 0.3
38 1 0.3 0.3
39 0 0.0 0.0
4 0 0.0 0.0
40 6 1.7 1.7
41 0 0.0 0.0
42 2 0.6 0.6
43 0 0.0 0.0
44 0 0.0 0.0
45 6 1.7 1.7
46 1 0.3 0.3
47 1 0.3 0.3
48 2 0.6 0.6
49 2 0.6 0.6
5 0 0.0 0.0
50 15 4.2 4.2
51 1 0.3 0.3
52 3 0.8 0.8
53 2 0.6 0.6
54 2 0.6 0.6
55 11 3.1 3.1
56 6 1.7 1.7
57 5 1.4 1.4
58 3 0.8 0.8
59 5 1.4 1.4
6 1 0.3 0.3
60 13 3.7 3.7
61 1 0.3 0.3
62 4 1.1 1.1
63 7 2.0 2.0
64 2 0.6 0.6
65 7 2.0 2.0
66 3 0.8 0.8
67 0 0.0 0.0
68 2 0.6 0.6
69 0 0.0 0.0
7 0 0.0 0.0
70 3 0.8 0.8
71 0 0.0 0.0
72 0 0.0 0.0
73 1 0.3 0.3
74 3 0.8 0.8
75 1 0.3 0.3
76 0 0.0 0.0
77 0 0.0 0.0
8 0 0.0 0.0
80 1 0.3 0.3
86 1 0.3 0.3
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 1 0.3 0.3
“NA” 212 59.6 59.6
Total 356 100.0 100.0
Asthma, COPD
n % val%
No 283 79.5 80.4
Yes 68 19.1 19.3
Scantron_Error 1 0.3 0.3
NA 4 1.1 NA
Total 356 100.0 100.0
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 3 0.8 0.8
10 3 0.8 0.8
11 1 0.3 0.3
12 0 0.0 0.0
13 0 0.0 0.0
14 2 0.6 0.6
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 1 0.3 0.3
19 2 0.6 0.6
2 0 0.0 0.0
20 1 0.3 0.3
21 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
26 1 0.3 0.3
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 1 0.3 0.3
33 0 0.0 0.0
35 1 0.3 0.3
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
5 4 1.1 1.1
50 2 0.6 0.6
51 1 0.3 0.3
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 6 1.7 1.7
57 4 1.1 1.1
58 2 0.6 0.6
59 1 0.3 0.3
6 0 0.0 0.0
60 4 1.1 1.1
61 0 0.0 0.0
62 2 0.6 0.6
63 1 0.3 0.3
64 2 0.6 0.6
65 1 0.3 0.3
66 2 0.6 0.6
67 1 0.3 0.3
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.3 0.3
71 1 0.3 0.3
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.3 0.3
75 1 0.3 0.3
77 0 0.0 0.0
78 1 0.3 0.3
79 1 0.3 0.3
8 0 0.0 0.0
80 0 0.0 0.0
81 1 0.3 0.3
9 1 0.3 0.3
93 0 0.0 0.0
97 0 0.0 0.0
“NA” 295 82.9 82.9
Total 356 100.0 100.0
Stomach ulcers
n % val%
No 321 90.2 90.9
Yes 32 9.0 9.1
Scantron_Error 0 0.0 0.0
NA 3 0.8 NA
Total 356 100.0 100.0
Stomach ulcers age
n % val%
0 10 2.8 2.8
10 0 0.0 0.0
13 0 0.0 0.0
14 1 0.3 0.3
15 0 0.0 0.0
16 0 0.0 0.0
17 1 0.3 0.3
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 1 0.3 0.3
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
3 0 0.0 0.0
30 1 0.3 0.3
32 0 0.0 0.0
34 1 0.3 0.3
35 1 0.3 0.3
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
44 0 0.0 0.0
45 1 0.3 0.3
46 1 0.3 0.3
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
50 2 0.6 0.6
52 0 0.0 0.0
53 0 0.0 0.0
54 1 0.3 0.3
55 0 0.0 0.0
56 0 0.0 0.0
57 1 0.3 0.3
58 0 0.0 0.0
59 0 0.0 0.0
60 2 0.6 0.6
61 0 0.0 0.0
62 1 0.3 0.3
63 1 0.3 0.3
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 1 0.3 0.3
9 0 0.0 0.0
94 0 0.0 0.0
“NA” 324 91.0 91.0
Total 356 100.0 100.0
Crohn’s Disease
n % val%
No 339 95.2 98
Yes 7 2.0 2
Scantron_Error 0 0.0 0
NA 10 2.8 NA
Total 356 100.0 100
Crohn’s Disease age
n % val%
15 0 0.0 0.0
2 0 0.0 0.0
22 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 1 0.3 0.3
35 0 0.0 0.0
37 0 0.0 0.0
39 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
44 1 0.3 0.3
45 0 0.0 0.0
46 0 0.0 0.0
48 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 0.3 0.3
62 0 0.0 0.0
63 0 0.0 0.0
64 1 0.3 0.3
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
79 0 0.0 0.0
85 0 0.0 0.0
“NA” 348 97.8 97.8
Total 356 100.0 100.0
Diabetes
n % val%
No 241 67.7 68.3
Yes 112 31.5 31.7
Scantron_Error 0 0.0 0.0
NA 3 0.8 NA
Total 356 100.0 100.0
Diabetes age
n % val%
0 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 1 0.3 0.3
22 1 0.3 0.3
24 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 3 0.8 0.8
3 0 0.0 0.0
30 3 0.8 0.8
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 4 1.1 1.1
36 1 0.3 0.3
37 0 0.0 0.0
38 1 0.3 0.3
39 1 0.3 0.3
40 2 0.6 0.6
41 0 0.0 0.0
42 3 0.8 0.8
43 2 0.6 0.6
44 0 0.0 0.0
45 3 0.8 0.8
46 0 0.0 0.0
47 2 0.6 0.6
48 1 0.3 0.3
49 1 0.3 0.3
5 0 0.0 0.0
50 9 2.5 2.5
51 0 0.0 0.0
52 1 0.3 0.3
53 1 0.3 0.3
54 2 0.6 0.6
55 7 2.0 2.0
56 2 0.6 0.6
57 6 1.7 1.7
58 3 0.8 0.8
59 1 0.3 0.3
60 8 2.2 2.2
61 1 0.3 0.3
62 4 1.1 1.1
63 3 0.8 0.8
64 1 0.3 0.3
65 4 1.1 1.1
66 1 0.3 0.3
67 1 0.3 0.3
68 2 0.6 0.6
69 3 0.8 0.8
70 2 0.6 0.6
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
78 0 0.0 0.0
81 1 0.3 0.3
94 0 0.0 0.0
95 0 0.0 0.0
97 0 0.0 0.0
“NA” 263 73.9 73.9
Total 356 100.0 100.0
Kidney Problems
n % val%
No 333 93.5 94.3
Yes 20 5.6 5.7
Scantron_Error 0 0.0 0.0
NA 3 0.8 NA
Total 356 100.0 100.0
Kidney Problems age
n % val%
11 0 0.0 0.0
15 2 0.6 0.6
16 0 0.0 0.0
17 0 0.0 0.0
29 1 0.3 0.3
35 0 0.0 0.0
38 0 0.0 0.0
40 1 0.3 0.3
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.3 0.3
49 0 0.0 0.0
5 0 0.0 0.0
50 2 0.6 0.6
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.3 0.3
56 0 0.0 0.0
57 2 0.6 0.6
58 0 0.0 0.0
59 2 0.6 0.6
60 1 0.3 0.3
61 1 0.3 0.3
62 1 0.3 0.3
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 1 0.3 0.3
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
83 0 0.0 0.0
95 0 0.0 0.0
“NA” 338 94.9 94.9
Total 356 100.0 100.0
Cirrhosis, liver damage
n % val%
No 345 96.9 97.7
Yes 8 2.2 2.3
Scantron_Error 0 0.0 0.0
NA 3 0.8 NA
Total 356 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 1 0.3 0.3
21 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
45 1 0.3 0.3
47 0 0.0 0.0
48 1 0.3 0.3
49 1 0.3 0.3
50 0 0.0 0.0
51 1 0.3 0.3
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
58 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 2 0.6 0.6
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
74 1 0.3 0.3
77 0 0.0 0.0
“NA” 348 97.8 97.8
Total 356 100.0 100.0
Arthritis
n % val%
No 298 83.7 84.9
Yes 53 14.9 15.1
Scantron_Error 0 0.0 0.0
NA 5 1.4 NA
Total 356 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 0 0.0 0.0
19 1 0.3 0.3
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 1 0.3 0.3
24 0 0.0 0.0
25 0 0.0 0.0
28 0 0.0 0.0
30 2 0.6 0.6
32 0 0.0 0.0
33 0 0.0 0.0
35 1 0.3 0.3
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 1 0.3 0.3
40 3 0.8 0.8
41 1 0.3 0.3
42 1 0.3 0.3
43 0 0.0 0.0
44 3 0.8 0.8
45 1 0.3 0.3
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 4 1.1 1.1
51 1 0.3 0.3
52 1 0.3 0.3
53 1 0.3 0.3
54 3 0.8 0.8
55 5 1.4 1.4
56 3 0.8 0.8
57 2 0.6 0.6
58 1 0.3 0.3
59 0 0.0 0.0
60 5 1.4 1.4
61 1 0.3 0.3
62 0 0.0 0.0
63 1 0.3 0.3
64 0 0.0 0.0
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 1 0.3 0.3
71 0 0.0 0.0
72 1 0.3 0.3
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 1 0.3 0.3
“NA” 309 86.8 86.8
Total 356 100.0 100.0
Dementia
n % val%
No 350 98.3 99.4
Yes 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 4 1.1 NA
Total 356 100.0 100.0
Dementia age
n % val%
10 0 0.0 0.0
29 0 0.0 0.0
53 1 0.3 0.3
57 0 0.0 0.0
60 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
80 0 0.0 0.0
“NA” 355 99.7 99.7
Total 356 100.0 100.0
Depression
n % val%
No 296 83.1 84.3
Yes 55 15.4 15.7
Scantron_Error 0 0.0 0.0
NA 5 1.4 NA
Total 356 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 0 0.0 0.0
16 1 0.3 0.3
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.3 0.3
20 3 0.8 0.8
21 0 0.0 0.0
22 0 0.0 0.0
24 1 0.3 0.3
25 1 0.3 0.3
26 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 1 0.3 0.3
34 0 0.0 0.0
35 1 0.3 0.3
36 1 0.3 0.3
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 1 0.3 0.3
41 0 0.0 0.0
42 1 0.3 0.3
43 1 0.3 0.3
44 1 0.3 0.3
45 2 0.6 0.6
46 0 0.0 0.0
47 1 0.3 0.3
48 0 0.0 0.0
49 0 0.0 0.0
50 4 1.1 1.1
51 0 0.0 0.0
52 1 0.3 0.3
53 1 0.3 0.3
54 2 0.6 0.6
55 2 0.6 0.6
56 1 0.3 0.3
57 2 0.6 0.6
58 2 0.6 0.6
59 1 0.3 0.3
6 0 0.0 0.0
60 2 0.6 0.6
61 2 0.6 0.6
62 0 0.0 0.0
63 1 0.3 0.3
64 1 0.3 0.3
65 1 0.3 0.3
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.3 0.3
75 1 0.3 0.3
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 1 0.3 0.3
“NA” 313 87.9 87.9
Total 356 100.0 100.0
AIDS
n % val%
No 343 96.3 99.1
Yes 3 0.8 0.9
Scantron_Error 0 0.0 0.0
NA 10 2.8 NA
Total 356 100.0 100.0
AIDS age
n % val%
0 10 2.8 2.8
25 0 0.0 0.0
30 0 0.0 0.0
33 1 0.3 0.3
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
45 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 1 0.3 0.3
51 0 0.0 0.0
55 0 0.0 0.0
59 1 0.3 0.3
60 0 0.0 0.0
63 0 0.0 0.0
65 0 0.0 0.0
9 0 0.0 0.0
90 0 0.0 0.0
“NA” 343 96.3 96.3
Total 356 100.0 100.0
Other Cancer
n % val%
No 319 89.6 93.8
Yes 21 5.9 6.2
Scantron_Error 0 0.0 0.0
NA 16 4.5 NA
Total 356 100.0 100.0
Other Cancer age
n % val%
10 1 0.3 0.3
12 0 0.0 0.0
16 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 1 0.3 0.3
24 0 0.0 0.0
30 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 2 0.6 0.6
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 1 0.3 0.3
51 2 0.6 0.6
52 0 0.0 0.0
54 1 0.3 0.3
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.3 0.3
59 2 0.6 0.6
60 0 0.0 0.0
61 0 0.0 0.0
62 1 0.3 0.3
63 2 0.6 0.6
64 0 0.0 0.0
65 1 0.3 0.3
66 3 0.8 0.8
67 0 0.0 0.0
68 1 0.3 0.3
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.3 0.3
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
“NA” 336 94.4 94.4
Total 356 100.0 100.0

Louisiana

Heart Attack
n % val%
No 494 84.4 91.8
Yes 44 7.5 8.2
Scantron_Error 0 0.0 0.0
NA 47 8.0 NA
Total 585 100.0 100.0
Heart Attack age
n % val%
0 1 0.2 0.2
1 0 0.0 0.0
14 1 0.2 0.2
17 0 0.0 0.0
20 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
29 0 0.0 0.0
31 0 0.0 0.0
32 1 0.2 0.2
34 0 0.0 0.0
35 0 0.0 0.0
38 1 0.2 0.2
40 0 0.0 0.0
42 0 0.0 0.0
44 2 0.3 0.3
45 0 0.0 0.0
46 1 0.2 0.2
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 2 0.3 0.3
51 4 0.7 0.7
52 1 0.2 0.2
53 0 0.0 0.0
54 2 0.3 0.3
55 1 0.2 0.2
56 2 0.3 0.3
57 1 0.2 0.2
58 0 0.0 0.0
59 3 0.5 0.5
60 2 0.3 0.3
61 0 0.0 0.0
62 1 0.2 0.2
63 0 0.0 0.0
64 3 0.5 0.5
65 2 0.3 0.3
66 1 0.2 0.2
67 0 0.0 0.0
68 0 0.0 0.0
69 2 0.3 0.3
70 3 0.5 0.5
71 0 0.0 0.0
72 1 0.2 0.2
73 0 0.0 0.0
74 1 0.2 0.2
76 1 0.2 0.2
79 0 0.0 0.0
93 1 0.2 0.2
“NA” 544 93.0 93.0
Total 585 100.0 100.0
Heart Failure or CHF
n % val%
No 496 84.8 93.4
Yes 35 6.0 6.6
Scantron_Error 0 0.0 0.0
NA 54 9.2 NA
Total 585 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
20 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 2 0.3 0.3
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 1 0.2 0.2
51 1 0.2 0.2
52 3 0.5 0.5
53 1 0.2 0.2
54 1 0.2 0.2
55 3 0.5 0.5
56 3 0.5 0.5
57 0 0.0 0.0
58 0 0.0 0.0
59 4 0.7 0.7
60 1 0.2 0.2
61 1 0.2 0.2
62 1 0.2 0.2
63 0 0.0 0.0
64 1 0.2 0.2
65 1 0.2 0.2
66 1 0.2 0.2
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 1 0.2 0.2
70 2 0.3 0.3
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
80 1 0.2 0.2
82 0 0.0 0.0
97 1 0.2 0.2
“NA” 555 94.9 94.9
Total 585 100.0 100.0
Stroke
n % val%
No 474 81.0 89.1
Yes 58 9.9 10.9
Scantron_Error 0 0.0 0.0
NA 53 9.1 NA
Total 585 100.0 100.0
Stroke age
n % val%
0 6 1.0 1.0
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 1 0.2 0.2
23 1 0.2 0.2
25 0 0.0 0.0
27 0 0.0 0.0
30 1 0.2 0.2
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 2 0.3 0.3
42 0 0.0 0.0
43 0 0.0 0.0
45 2 0.3 0.3
46 1 0.2 0.2
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 1 0.2 0.2
50 1 0.2 0.2
51 2 0.3 0.3
52 2 0.3 0.3
53 1 0.2 0.2
54 6 1.0 1.0
55 1 0.2 0.2
56 3 0.5 0.5
57 4 0.7 0.7
58 1 0.2 0.2
59 1 0.2 0.2
60 7 1.2 1.2
61 1 0.2 0.2
62 2 0.3 0.3
63 3 0.5 0.5
64 1 0.2 0.2
65 2 0.3 0.3
66 1 0.2 0.2
67 0 0.0 0.0
68 2 0.3 0.3
69 1 0.2 0.2
70 3 0.5 0.5
71 1 0.2 0.2
72 1 0.2 0.2
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
“NA” 523 89.4 89.4
Total 585 100.0 100.0
Hypertension
n % val%
No 130 22.2 23.4
Yes 420 71.8 75.7
Scantron_Error 5 0.9 0.9
NA 30 5.1 NA
Total 585 100.0 100.0
Hypertension age
n % val%
.35 0 0.0 0.0
1 0 0.0 0.0
10 0 0.0 0.0
12 2 0.3 0.3
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 1 0.2 0.2
17 0 0.0 0.0
18 1 0.2 0.2
19 1 0.2 0.2
2 0 0.0 0.0
20 3 0.5 0.5
21 0 0.0 0.0
22 0 0.0 0.0
23 1 0.2 0.2
24 1 0.2 0.2
25 3 0.5 0.5
26 0 0.0 0.0
27 0 0.0 0.0
28 2 0.3 0.3
29 0 0.0 0.0
30 6 1.0 1.0
31 2 0.3 0.3
32 1 0.2 0.2
33 2 0.3 0.3
34 1 0.2 0.2
35 6 1.0 1.0
36 4 0.7 0.7
37 1 0.2 0.2
38 5 0.9 0.9
39 3 0.5 0.5
4 0 0.0 0.0
40 20 3.4 3.4
41 2 0.3 0.3
42 5 0.9 0.9
43 3 0.5 0.5
44 4 0.7 0.7
45 23 3.9 3.9
46 6 1.0 1.0
47 2 0.3 0.3
48 10 1.7 1.7
49 5 0.9 0.9
5 2 0.3 0.3
50 46 7.9 7.9
51 7 1.2 1.2
52 4 0.7 0.7
53 2 0.3 0.3
54 7 1.2 1.2
55 17 2.9 2.9
56 16 2.7 2.7
57 9 1.5 1.5
58 12 2.1 2.1
59 9 1.5 1.5
6 0 0.0 0.0
60 35 6.0 6.0
61 4 0.7 0.7
62 10 1.7 1.7
63 3 0.5 0.5
64 6 1.0 1.0
65 11 1.9 1.9
66 4 0.7 0.7
67 4 0.7 0.7
68 3 0.5 0.5
69 1 0.2 0.2
7 1 0.2 0.2
70 8 1.4 1.4
71 3 0.5 0.5
72 3 0.5 0.5
73 2 0.3 0.3
74 1 0.2 0.2
75 1 0.2 0.2
77 0 0.0 0.0
78 0 0.0 0.0
79 1 0.2 0.2
8 1 0.2 0.2
80 0 0.0 0.0
89 0 0.0 0.0
9 1 0.2 0.2
92 0 0.0 0.0
94 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
99 1 0.2 0.2
“NA” 224 38.3 38.3
Total 585 100.0 100.0
Peripheral arterial disease
n % val%
No 475 81.2 91.3
Yes 45 7.7 8.7
Scantron_Error 0 0.0 0.0
NA 65 11.1 NA
Total 585 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 3 0.5 0.5
1 0 0.0 0.0
16 0 0.0 0.0
17 1 0.2 0.2
19 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
40 3 0.5 0.5
41 1 0.2 0.2
42 0 0.0 0.0
44 2 0.3 0.3
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
5 1 0.2 0.2
50 5 0.9 0.9
51 1 0.2 0.2
52 0 0.0 0.0
53 2 0.3 0.3
54 2 0.3 0.3
55 3 0.5 0.5
56 2 0.3 0.3
57 3 0.5 0.5
58 1 0.2 0.2
59 2 0.3 0.3
60 2 0.3 0.3
61 1 0.2 0.2
62 1 0.2 0.2
63 1 0.2 0.2
64 4 0.7 0.7
65 1 0.2 0.2
66 0 0.0 0.0
67 0 0.0 0.0
68 1 0.2 0.2
69 1 0.2 0.2
70 3 0.5 0.5
71 0 0.0 0.0
72 1 0.2 0.2
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
77 1 0.2 0.2
78 0 0.0 0.0
82 0 0.0 0.0
84 0 0.0 0.0
94 1 0.2 0.2
97 0 0.0 0.0
“NA” 535 91.5 91.5
Total 585 100.0 100.0
High Cholesterol
n % val%
No 220 37.6 40.6
Yes 321 54.9 59.2
Scantron_Error 1 0.2 0.2
NA 43 7.4 NA
Total 585 100.0 100.0
High Cholesterol age
n % val%
1 0 0.0 0.0
10 2 0.3 0.3
12 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 1 0.2 0.2
18 0 0.0 0.0
19 1 0.2 0.2
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 1 0.2 0.2
25 1 0.2 0.2
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 1 0.2 0.2
33 0 0.0 0.0
34 2 0.3 0.3
35 5 0.9 0.9
36 3 0.5 0.5
37 1 0.2 0.2
38 3 0.5 0.5
39 2 0.3 0.3
4 0 0.0 0.0
40 5 0.9 0.9
41 2 0.3 0.3
42 2 0.3 0.3
43 2 0.3 0.3
44 4 0.7 0.7
45 12 2.1 2.1
46 3 0.5 0.5
47 1 0.2 0.2
48 6 1.0 1.0
49 2 0.3 0.3
5 2 0.3 0.3
50 24 4.1 4.1
51 7 1.2 1.2
52 6 1.0 1.0
53 4 0.7 0.7
54 8 1.4 1.4
55 21 3.6 3.6
56 9 1.5 1.5
57 7 1.2 1.2
58 5 0.9 0.9
59 6 1.0 1.0
6 0 0.0 0.0
60 24 4.1 4.1
61 7 1.2 1.2
62 11 1.9 1.9
63 4 0.7 0.7
64 5 0.9 0.9
65 11 1.9 1.9
66 2 0.3 0.3
67 2 0.3 0.3
68 7 1.2 1.2
69 3 0.5 0.5
7 1 0.2 0.2
70 8 1.4 1.4
71 3 0.5 0.5
72 1 0.2 0.2
73 2 0.3 0.3
74 1 0.2 0.2
75 3 0.5 0.5
76 0 0.0 0.0
77 1 0.2 0.2
8 0 0.0 0.0
80 0 0.0 0.0
86 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 1 0.2 0.2
“NA” 327 55.9 55.9
Total 585 100.0 100.0
Asthma, COPD
n % val%
No 472 80.7 83.5
Yes 92 15.7 16.3
Scantron_Error 1 0.2 0.2
NA 20 3.4 NA
Total 585 100.0 100.0
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 2 0.3 0.3
10 2 0.3 0.3
11 2 0.3 0.3
12 3 0.5 0.5
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 2 0.3 0.3
18 0 0.0 0.0
19 0 0.0 0.0
2 2 0.3 0.3
20 3 0.5 0.5
21 1 0.2 0.2
23 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 1 0.2 0.2
30 1 0.2 0.2
31 1 0.2 0.2
32 0 0.0 0.0
33 0 0.0 0.0
35 1 0.2 0.2
37 0 0.0 0.0
38 0 0.0 0.0
39 1 0.2 0.2
4 2 0.3 0.3
40 1 0.2 0.2
42 1 0.2 0.2
43 0 0.0 0.0
45 1 0.2 0.2
46 0 0.0 0.0
47 0 0.0 0.0
48 1 0.2 0.2
49 0 0.0 0.0
5 0 0.0 0.0
50 6 1.0 1.0
51 2 0.3 0.3
52 0 0.0 0.0
53 1 0.2 0.2
54 1 0.2 0.2
55 2 0.3 0.3
56 2 0.3 0.3
57 2 0.3 0.3
58 1 0.2 0.2
59 1 0.2 0.2
6 2 0.3 0.3
60 7 1.2 1.2
61 0 0.0 0.0
62 2 0.3 0.3
63 4 0.7 0.7
64 0 0.0 0.0
65 2 0.3 0.3
66 2 0.3 0.3
67 1 0.2 0.2
68 1 0.2 0.2
69 1 0.2 0.2
7 2 0.3 0.3
70 3 0.5 0.5
71 1 0.2 0.2
72 0 0.0 0.0
73 1 0.2 0.2
74 1 0.2 0.2
75 2 0.3 0.3
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 2 0.3 0.3
80 1 0.2 0.2
81 0 0.0 0.0
9 0 0.0 0.0
93 1 0.2 0.2
97 0 0.0 0.0
“NA” 503 86.0 86.0
Total 585 100.0 100.0
Stomach ulcers
n % val%
No 503 86.0 90.3
Yes 53 9.1 9.5
Scantron_Error 1 0.2 0.2
NA 28 4.8 NA
Total 585 100.0 100.0
Stomach ulcers age
n % val%
0 2 0.3 0.3
10 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 1 0.2 0.2
19 0 0.0 0.0
2 0 0.0 0.0
20 1 0.2 0.2
21 0 0.0 0.0
22 2 0.3 0.3
23 0 0.0 0.0
24 1 0.2 0.2
25 2 0.3 0.3
27 1 0.2 0.2
28 0 0.0 0.0
3 0 0.0 0.0
30 5 0.9 0.9
32 0 0.0 0.0
34 0 0.0 0.0
35 4 0.7 0.7
36 1 0.2 0.2
38 0 0.0 0.0
39 1 0.2 0.2
40 0 0.0 0.0
42 2 0.3 0.3
44 0 0.0 0.0
45 2 0.3 0.3
46 1 0.2 0.2
47 0 0.0 0.0
48 1 0.2 0.2
49 0 0.0 0.0
50 4 0.7 0.7
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.2 0.2
56 0 0.0 0.0
57 1 0.2 0.2
58 1 0.2 0.2
59 1 0.2 0.2
60 5 0.9 0.9
61 0 0.0 0.0
62 1 0.2 0.2
63 1 0.2 0.2
64 1 0.2 0.2
65 1 0.2 0.2
66 1 0.2 0.2
67 1 0.2 0.2
68 1 0.2 0.2
69 0 0.0 0.0
7 1 0.2 0.2
70 1 0.2 0.2
71 0 0.0 0.0
72 0 0.0 0.0
73 1 0.2 0.2
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
9 1 0.2 0.2
94 0 0.0 0.0
“NA” 534 91.3 91.3
Total 585 100.0 100.0
Crohn’s Disease
n % val%
No 542 92.6 97
Yes 17 2.9 3
Scantron_Error 0 0.0 0
NA 26 4.4 NA
Total 585 100.0 100
Crohn’s Disease age
n % val%
15 0 0.0 0.0
2 1 0.2 0.2
22 1 0.2 0.2
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
37 0 0.0 0.0
39 0 0.0 0.0
40 2 0.3 0.3
41 0 0.0 0.0
44 1 0.2 0.2
45 0 0.0 0.0
46 0 0.0 0.0
48 0 0.0 0.0
50 2 0.3 0.3
51 1 0.2 0.2
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 0.2 0.2
56 0 0.0 0.0
57 1 0.2 0.2
58 2 0.3 0.3
59 0 0.0 0.0
60 1 0.2 0.2
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 1 0.2 0.2
69 1 0.2 0.2
70 0 0.0 0.0
71 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
79 1 0.2 0.2
85 0 0.0 0.0
“NA” 569 97.3 97.3
Total 585 100.0 100.0
Diabetes
n % val%
No 366 62.6 64.6
Yes 200 34.2 35.3
Scantron_Error 1 0.2 0.2
NA 18 3.1 NA
Total 585 100.0 100.0
Diabetes age
n % val%
0 3 0.5 0.5
10 0 0.0 0.0
12 1 0.2 0.2
13 0 0.0 0.0
14 1 0.2 0.2
15 0 0.0 0.0
16 0 0.0 0.0
17 1 0.2 0.2
18 1 0.2 0.2
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 1 0.2 0.2
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 1 0.2 0.2
36 2 0.3 0.3
37 1 0.2 0.2
38 2 0.3 0.3
39 1 0.2 0.2
40 7 1.2 1.2
41 0 0.0 0.0
42 1 0.2 0.2
43 3 0.5 0.5
44 0 0.0 0.0
45 7 1.2 1.2
46 1 0.2 0.2
47 0 0.0 0.0
48 5 0.9 0.9
49 3 0.5 0.5
5 0 0.0 0.0
50 12 2.1 2.1
51 3 0.5 0.5
52 3 0.5 0.5
53 3 0.5 0.5
54 4 0.7 0.7
55 16 2.7 2.7
56 5 0.9 0.9
57 4 0.7 0.7
58 11 1.9 1.9
59 7 1.2 1.2
60 15 2.6 2.6
61 7 1.2 1.2
62 7 1.2 1.2
63 1 0.2 0.2
64 5 0.9 0.9
65 5 0.9 0.9
66 2 0.3 0.3
67 0 0.0 0.0
68 3 0.5 0.5
69 4 0.7 0.7
70 7 1.2 1.2
71 1 0.2 0.2
72 0 0.0 0.0
73 0 0.0 0.0
74 1 0.2 0.2
75 1 0.2 0.2
76 0 0.0 0.0
78 0 0.0 0.0
81 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
97 0 0.0 0.0
“NA” 416 71.1 71.1
Total 585 100.0 100.0
Kidney Problems
n % val%
No 532 90.9 94.7
Yes 30 5.1 5.3
Scantron_Error 0 0.0 0.0
NA 23 3.9 NA
Total 585 100.0 100.0
Kidney Problems age
n % val%
11 1 0.2 0.2
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
29 0 0.0 0.0
35 0 0.0 0.0
38 1 0.2 0.2
40 0 0.0 0.0
42 0 0.0 0.0
44 1 0.2 0.2
45 1 0.2 0.2
46 0 0.0 0.0
47 1 0.2 0.2
48 0 0.0 0.0
49 0 0.0 0.0
5 2 0.3 0.3
50 1 0.2 0.2
51 1 0.2 0.2
52 0 0.0 0.0
53 1 0.2 0.2
54 0 0.0 0.0
55 2 0.3 0.3
56 0 0.0 0.0
57 0 0.0 0.0
58 1 0.2 0.2
59 0 0.0 0.0
60 1 0.2 0.2
61 1 0.2 0.2
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 2 0.3 0.3
66 0 0.0 0.0
67 1 0.2 0.2
68 1 0.2 0.2
69 2 0.3 0.3
7 0 0.0 0.0
70 1 0.2 0.2
71 0 0.0 0.0
72 0 0.0 0.0
74 0 0.0 0.0
75 1 0.2 0.2
76 0 0.0 0.0
77 1 0.2 0.2
80 0 0.0 0.0
83 0 0.0 0.0
95 1 0.2 0.2
“NA” 560 95.7 95.7
Total 585 100.0 100.0
Cirrhosis, liver damage
n % val%
No 550 94.0 97.7
Yes 13 2.2 2.3
Scantron_Error 0 0.0 0.0
NA 22 3.8 NA
Total 585 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 0 0.0 0.0
21 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
45 0 0.0 0.0
47 1 0.2 0.2
48 1 0.2 0.2
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
53 1 0.2 0.2
54 0 0.0 0.0
55 1 0.2 0.2
58 0 0.0 0.0
60 1 0.2 0.2
61 1 0.2 0.2
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.2 0.2
66 1 0.2 0.2
67 1 0.2 0.2
68 0 0.0 0.0
69 0 0.0 0.0
74 0 0.0 0.0
77 1 0.2 0.2
“NA” 575 98.3 98.3
Total 585 100.0 100.0
Arthritis
n % val%
No 472 80.7 84.3
Yes 88 15.0 15.7
Scantron_Error 0 0.0 0.0
NA 25 4.3 NA
Total 585 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 1 0.2 0.2
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 1 0.2 0.2
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
28 0 0.0 0.0
30 1 0.2 0.2
32 0 0.0 0.0
33 0 0.0 0.0
35 3 0.5 0.5
36 0 0.0 0.0
38 2 0.3 0.3
39 2 0.3 0.3
4 0 0.0 0.0
40 2 0.3 0.3
41 1 0.2 0.2
42 0 0.0 0.0
43 1 0.2 0.2
44 1 0.2 0.2
45 6 1.0 1.0
46 0 0.0 0.0
47 0 0.0 0.0
48 3 0.5 0.5
49 0 0.0 0.0
50 7 1.2 1.2
51 1 0.2 0.2
52 6 1.0 1.0
53 2 0.3 0.3
54 1 0.2 0.2
55 4 0.7 0.7
56 0 0.0 0.0
57 0 0.0 0.0
58 4 0.7 0.7
59 1 0.2 0.2
60 5 0.9 0.9
61 0 0.0 0.0
62 2 0.3 0.3
63 1 0.2 0.2
64 3 0.5 0.5
65 1 0.2 0.2
66 0 0.0 0.0
67 1 0.2 0.2
68 2 0.3 0.3
69 2 0.3 0.3
7 0 0.0 0.0
70 0 0.0 0.0
71 1 0.2 0.2
72 2 0.3 0.3
73 0 0.0 0.0
74 1 0.2 0.2
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
“NA” 514 87.9 87.9
Total 585 100.0 100.0
Dementia
n % val%
No 559 95.6 98.4
Yes 9 1.5 1.6
Scantron_Error 0 0.0 0.0
NA 17 2.9 NA
Total 585 100.0 100.0
Dementia age
n % val%
10 0 0.0 0.0
29 0 0.0 0.0
53 0 0.0 0.0
57 0 0.0 0.0
60 0 0.0 0.0
62 1 0.2 0.2
63 0 0.0 0.0
64 0 0.0 0.0
65 1 0.2 0.2
66 0 0.0 0.0
67 0 0.0 0.0
69 1 0.2 0.2
70 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
80 1 0.2 0.2
“NA” 581 99.3 99.3
Total 585 100.0 100.0
Depression
n % val%
No 486 83.1 87.1
Yes 72 12.3 12.9
Scantron_Error 0 0.0 0.0
NA 27 4.6 NA
Total 585 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
20 2 0.3 0.3
21 2 0.3 0.3
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 1 0.2 0.2
28 1 0.2 0.2
29 0 0.0 0.0
30 2 0.3 0.3
32 1 0.2 0.2
33 0 0.0 0.0
34 0 0.0 0.0
35 2 0.3 0.3
36 0 0.0 0.0
37 0 0.0 0.0
38 1 0.2 0.2
39 0 0.0 0.0
4 0 0.0 0.0
40 4 0.7 0.7
41 1 0.2 0.2
42 3 0.5 0.5
43 0 0.0 0.0
44 0 0.0 0.0
45 1 0.2 0.2
46 1 0.2 0.2
47 2 0.3 0.3
48 0 0.0 0.0
49 0 0.0 0.0
50 6 1.0 1.0
51 1 0.2 0.2
52 2 0.3 0.3
53 1 0.2 0.2
54 1 0.2 0.2
55 3 0.5 0.5
56 1 0.2 0.2
57 0 0.0 0.0
58 1 0.2 0.2
59 1 0.2 0.2
6 0 0.0 0.0
60 3 0.5 0.5
61 2 0.3 0.3
62 3 0.5 0.5
63 0 0.0 0.0
64 0 0.0 0.0
65 3 0.5 0.5
66 1 0.2 0.2
67 0 0.0 0.0
68 2 0.3 0.3
69 0 0.0 0.0
7 1 0.2 0.2
70 3 0.5 0.5
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
“NA” 526 89.9 89.9
Total 585 100.0 100.0
AIDS
n % val%
No 562 96.1 99.5
Yes 3 0.5 0.5
Scantron_Error 0 0.0 0.0
NA 20 3.4 NA
Total 585 100.0 100.0
AIDS age
n % val%
0 2 0.3 0.3
25 0 0.0 0.0
30 0 0.0 0.0
33 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
45 1 0.2 0.2
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
55 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
63 0 0.0 0.0
65 0 0.0 0.0
9 0 0.0 0.0
90 0 0.0 0.0
“NA” 582 99.5 99.5
Total 585 100.0 100.0
Other Cancer
n % val%
No 522 89.2 93.2
Yes 38 6.5 6.8
Scantron_Error 0 0.0 0.0
NA 25 4.3 NA
Total 585 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 1 0.2 0.2
16 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 1 0.2 0.2
20 1 0.2 0.2
22 0 0.0 0.0
24 0 0.0 0.0
30 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 1 0.2 0.2
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 2 0.3 0.3
43 1 0.2 0.2
44 1 0.2 0.2
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 1 0.2 0.2
50 1 0.2 0.2
51 0 0.0 0.0
52 1 0.2 0.2
54 0 0.0 0.0
55 1 0.2 0.2
56 2 0.3 0.3
57 1 0.2 0.2
58 2 0.3 0.3
59 0 0.0 0.0
60 1 0.2 0.2
61 2 0.3 0.3
62 2 0.3 0.3
63 1 0.2 0.2
64 1 0.2 0.2
65 1 0.2 0.2
66 1 0.2 0.2
67 0 0.0 0.0
68 2 0.3 0.3
69 2 0.3 0.3
7 0 0.0 0.0
70 1 0.2 0.2
71 1 0.2 0.2
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 1 0.2 0.2
“NA” 552 94.4 94.4
Total 585 100.0 100.0

Georgia

Heart Attack
n % val%
No 1532 87.3 93.2
Yes 110 6.3 6.7
Scantron_Error 2 0.1 0.1
NA 110 6.3 NA
Total 1754 100.0 100.0
Heart Attack age
n % val%
0 21 1.2 1.2
1 1 0.1 0.1
14 0 0.0 0.0
17 1 0.1 0.1
20 0 0.0 0.0
24 1 0.1 0.1
25 1 0.1 0.1
26 0 0.0 0.0
27 2 0.1 0.1
29 1 0.1 0.1
31 2 0.1 0.1
32 0 0.0 0.0
34 1 0.1 0.1
35 2 0.1 0.1
38 1 0.1 0.1
40 2 0.1 0.1
42 0 0.0 0.0
44 0 0.0 0.0
45 1 0.1 0.1
46 3 0.2 0.2
47 1 0.1 0.1
48 3 0.2 0.2
49 1 0.1 0.1
5 1 0.1 0.1
50 8 0.5 0.5
51 0 0.0 0.0
52 3 0.2 0.2
53 1 0.1 0.1
54 1 0.1 0.1
55 4 0.2 0.2
56 1 0.1 0.1
57 4 0.2 0.2
58 6 0.3 0.3
59 6 0.3 0.3
60 5 0.3 0.3
61 3 0.2 0.2
62 1 0.1 0.1
63 5 0.3 0.3
64 4 0.2 0.2
65 2 0.1 0.1
66 1 0.1 0.1
67 3 0.2 0.2
68 2 0.1 0.1
69 3 0.2 0.2
70 1 0.1 0.1
71 2 0.1 0.1
72 0 0.0 0.0
73 2 0.1 0.1
74 3 0.2 0.2
76 0 0.0 0.0
79 0 0.0 0.0
93 0 0.0 0.0
“NA” 1637 93.3 93.3
Total 1754 100.0 100.0
Heart Failure or CHF
n % val%
No 1516 86.4 93.5
Yes 105 6.0 6.5
Scantron_Error 0 0.0 0.0
NA 133 7.6 NA
Total 1754 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
20 1 0.1 0.1
25 0 0.0 0.0
27 1 0.1 0.1
28 0 0.0 0.0
29 0 0.0 0.0
30 1 0.1 0.1
31 1 0.1 0.1
34 1 0.1 0.1
35 1 0.1 0.1
39 1 0.1 0.1
40 2 0.1 0.1
42 0 0.0 0.0
43 2 0.1 0.1
45 0 0.0 0.0
46 0 0.0 0.0
47 1 0.1 0.1
48 3 0.2 0.2
49 5 0.3 0.3
5 1 0.1 0.1
50 2 0.1 0.1
51 1 0.1 0.1
52 3 0.2 0.2
53 2 0.1 0.1
54 2 0.1 0.1
55 4 0.2 0.2
56 1 0.1 0.1
57 2 0.1 0.1
58 5 0.3 0.3
59 4 0.2 0.2
60 4 0.2 0.2
61 3 0.2 0.2
62 4 0.2 0.2
63 2 0.1 0.1
64 0 0.0 0.0
65 6 0.3 0.3
66 2 0.1 0.1
67 3 0.2 0.2
68 2 0.1 0.1
69 5 0.3 0.3
7 0 0.0 0.0
70 1 0.1 0.1
71 1 0.1 0.1
72 2 0.1 0.1
73 1 0.1 0.1
74 2 0.1 0.1
75 1 0.1 0.1
77 0 0.0 0.0
78 0 0.0 0.0
80 0 0.0 0.0
82 1 0.1 0.1
97 0 0.0 0.0
“NA” 1667 95.0 95.0
Total 1754 100.0 100.0
Stroke
n % val%
No 1473 84.0 90.6
Yes 150 8.6 9.2
Scantron_Error 3 0.2 0.2
NA 128 7.3 NA
Total 1754 100.0 100.0
Stroke age
n % val%
0 36 2.1 2.1
15 1 0.1 0.1
16 1 0.1 0.1
19 1 0.1 0.1
20 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
30 1 0.1 0.1
34 1 0.1 0.1
35 1 0.1 0.1
38 1 0.1 0.1
40 1 0.1 0.1
42 1 0.1 0.1
43 2 0.1 0.1
45 3 0.2 0.2
46 2 0.1 0.1
47 0 0.0 0.0
48 1 0.1 0.1
49 1 0.1 0.1
5 1 0.1 0.1
50 7 0.4 0.4
51 2 0.1 0.1
52 1 0.1 0.1
53 2 0.1 0.1
54 4 0.2 0.2
55 7 0.4 0.4
56 4 0.2 0.2
57 4 0.2 0.2
58 3 0.2 0.2
59 5 0.3 0.3
60 5 0.3 0.3
61 6 0.3 0.3
62 5 0.3 0.3
63 9 0.5 0.5
64 4 0.2 0.2
65 7 0.4 0.4
66 4 0.2 0.2
67 5 0.3 0.3
68 3 0.2 0.2
69 7 0.4 0.4
70 2 0.1 0.1
71 2 0.1 0.1
72 0 0.0 0.0
73 1 0.1 0.1
74 2 0.1 0.1
75 1 0.1 0.1
78 2 0.1 0.1
79 1 0.1 0.1
8 1 0.1 0.1
“NA” 1593 90.8 90.8
Total 1754 100.0 100.0
Hypertension
n % val%
No 398 22.7 23.8
Yes 1270 72.4 76.0
Scantron_Error 2 0.1 0.1
NA 84 4.8 NA
Total 1754 100.0 100.0
Hypertension age
n % val%
.35 0 0.0 0.0
1 1 0.1 0.1
10 0 0.0 0.0
12 1 0.1 0.1
13 1 0.1 0.1
14 1 0.1 0.1
15 3 0.2 0.2
16 6 0.3 0.3
17 1 0.1 0.1
18 4 0.2 0.2
19 3 0.2 0.2
2 1 0.1 0.1
20 6 0.3 0.3
21 2 0.1 0.1
22 3 0.2 0.2
23 3 0.2 0.2
24 4 0.2 0.2
25 10 0.6 0.6
26 1 0.1 0.1
27 5 0.3 0.3
28 6 0.3 0.3
29 1 0.1 0.1
30 37 2.1 2.1
31 9 0.5 0.5
32 4 0.2 0.2
33 4 0.2 0.2
34 4 0.2 0.2
35 47 2.7 2.7
36 10 0.6 0.6
37 4 0.2 0.2
38 10 0.6 0.6
39 7 0.4 0.4
4 1 0.1 0.1
40 90 5.1 5.1
41 5 0.3 0.3
42 18 1.0 1.0
43 7 0.4 0.4
44 9 0.5 0.5
45 76 4.3 4.3
46 12 0.7 0.7
47 10 0.6 0.6
48 17 1.0 1.0
49 14 0.8 0.8
5 1 0.1 0.1
50 115 6.6 6.6
51 11 0.6 0.6
52 28 1.6 1.6
53 11 0.6 0.6
54 24 1.4 1.4
55 80 4.6 4.6
56 21 1.2 1.2
57 18 1.0 1.0
58 28 1.6 1.6
59 18 1.0 1.0
6 0 0.0 0.0
60 76 4.3 4.3
61 15 0.9 0.9
62 24 1.4 1.4
63 12 0.7 0.7
64 19 1.1 1.1
65 30 1.7 1.7
66 6 0.3 0.3
67 14 0.8 0.8
68 12 0.7 0.7
69 9 0.5 0.5
7 0 0.0 0.0
70 15 0.9 0.9
71 9 0.5 0.5
72 3 0.2 0.2
73 2 0.1 0.1
74 0 0.0 0.0
75 1 0.1 0.1
77 1 0.1 0.1
78 0 0.0 0.0
79 0 0.0 0.0
8 2 0.1 0.1
80 0 0.0 0.0
89 1 0.1 0.1
9 0 0.0 0.0
92 1 0.1 0.1
94 1 0.1 0.1
96 1 0.1 0.1
98 0 0.0 0.0
99 1 0.1 0.1
“NA” 686 39.1 39.1
Total 1754 100.0 100.0
Peripheral arterial disease
n % val%
No 1488 84.8 93.4
Yes 104 5.9 6.5
Scantron_Error 1 0.1 0.1
NA 161 9.2 NA
Total 1754 100.0 100.0
Peripheral arterial disease age
n % val%
* 1 0.1 0.1
** 1 0.1 0.1
0 16 0.9 0.9
1 1 0.1 0.1
16 1 0.1 0.1
17 0 0.0 0.0
19 1 0.1 0.1
25 0 0.0 0.0
26 1 0.1 0.1
30 1 0.1 0.1
31 0 0.0 0.0
33 1 0.1 0.1
34 2 0.1 0.1
35 4 0.2 0.2
36 0 0.0 0.0
37 1 0.1 0.1
40 7 0.4 0.4
41 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 3 0.2 0.2
46 0 0.0 0.0
47 1 0.1 0.1
48 1 0.1 0.1
5 0 0.0 0.0
50 8 0.5 0.5
51 0 0.0 0.0
52 1 0.1 0.1
53 0 0.0 0.0
54 3 0.2 0.2
55 8 0.5 0.5
56 1 0.1 0.1
57 1 0.1 0.1
58 5 0.3 0.3
59 3 0.2 0.2
60 14 0.8 0.8
61 0 0.0 0.0
62 6 0.3 0.3
63 2 0.1 0.1
64 0 0.0 0.0
65 10 0.6 0.6
66 2 0.1 0.1
67 5 0.3 0.3
68 3 0.2 0.2
69 3 0.2 0.2
70 1 0.1 0.1
71 1 0.1 0.1
72 2 0.1 0.1
73 2 0.1 0.1
74 2 0.1 0.1
76 0 0.0 0.0
77 0 0.0 0.0
78 2 0.1 0.1
82 0 0.0 0.0
84 1 0.1 0.1
94 0 0.0 0.0
97 1 0.1 0.1
“NA” 1624 92.6 92.6
Total 1754 100.0 100.0
High Cholesterol
n % val%
No 720 41.0 44.0
Yes 916 52.2 56.0
Scantron_Error 1 0.1 0.1
NA 117 6.7 NA
Total 1754 100.0 100.0
High Cholesterol age
n % val%
1 2 0.1 0.1
10 1 0.1 0.1
12 1 0.1 0.1
14 1 0.1 0.1
15 1 0.1 0.1
16 2 0.1 0.1
17 0 0.0 0.0
18 0 0.0 0.0
19 1 0.1 0.1
2 1 0.1 0.1
20 1 0.1 0.1
21 2 0.1 0.1
22 1 0.1 0.1
24 3 0.2 0.2
25 3 0.2 0.2
26 0 0.0 0.0
27 3 0.2 0.2
28 4 0.2 0.2
29 0 0.0 0.0
30 10 0.6 0.6
31 4 0.2 0.2
32 3 0.2 0.2
33 0 0.0 0.0
34 3 0.2 0.2
35 18 1.0 1.0
36 8 0.5 0.5
37 2 0.1 0.1
38 9 0.5 0.5
39 2 0.1 0.1
4 1 0.1 0.1
40 45 2.6 2.6
41 3 0.2 0.2
42 3 0.2 0.2
43 4 0.2 0.2
44 4 0.2 0.2
45 42 2.4 2.4
46 8 0.5 0.5
47 7 0.4 0.4
48 12 0.7 0.7
49 7 0.4 0.4
5 1 0.1 0.1
50 90 5.1 5.1
51 7 0.4 0.4
52 12 0.7 0.7
53 9 0.5 0.5
54 18 1.0 1.0
55 65 3.7 3.7
56 16 0.9 0.9
57 22 1.3 1.3
58 14 0.8 0.8
59 16 0.9 0.9
6 0 0.0 0.0
60 66 3.8 3.8
61 11 0.6 0.6
62 31 1.8 1.8
63 10 0.6 0.6
64 11 0.6 0.6
65 24 1.4 1.4
66 7 0.4 0.4
67 11 0.6 0.6
68 8 0.5 0.5
69 16 0.9 0.9
7 0 0.0 0.0
70 14 0.8 0.8
71 2 0.1 0.1
72 8 0.5 0.5
73 2 0.1 0.1
74 3 0.2 0.2
75 2 0.1 0.1
76 3 0.2 0.2
77 0 0.0 0.0
8 1 0.1 0.1
80 0 0.0 0.0
86 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
95 1 0.1 0.1
96 1 0.1 0.1
97 1 0.1 0.1
99 0 0.0 0.0
“NA” 1029 58.7 58.7
Total 1754 100.0 100.0
Asthma, COPD
n % val%
No 1464 83.5 86.3
Yes 232 13.2 13.7
Scantron_Error 0 0.0 0.0
NA 58 3.3 NA
Total 1754 100.0 100.0
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 4 0.2 0.2
10 9 0.5 0.5
11 1 0.1 0.1
12 6 0.3 0.3
13 1 0.1 0.1
14 1 0.1 0.1
15 3 0.2 0.2
16 3 0.2 0.2
17 0 0.0 0.0
18 8 0.5 0.5
19 0 0.0 0.0
2 3 0.2 0.2
20 0 0.0 0.0
21 1 0.1 0.1
23 1 0.1 0.1
25 3 0.2 0.2
26 0 0.0 0.0
27 1 0.1 0.1
28 2 0.1 0.1
29 1 0.1 0.1
3 0 0.0 0.0
30 4 0.2 0.2
31 0 0.0 0.0
32 1 0.1 0.1
33 1 0.1 0.1
35 1 0.1 0.1
37 1 0.1 0.1
38 3 0.2 0.2
39 0 0.0 0.0
4 3 0.2 0.2
40 6 0.3 0.3
42 3 0.2 0.2
43 1 0.1 0.1
45 4 0.2 0.2
46 1 0.1 0.1
47 1 0.1 0.1
48 1 0.1 0.1
49 2 0.1 0.1
5 12 0.7 0.7
50 7 0.4 0.4
51 1 0.1 0.1
52 3 0.2 0.2
53 1 0.1 0.1
54 1 0.1 0.1
55 9 0.5 0.5
56 3 0.2 0.2
57 5 0.3 0.3
58 3 0.2 0.2
59 6 0.3 0.3
6 7 0.4 0.4
60 9 0.5 0.5
61 2 0.1 0.1
62 5 0.3 0.3
63 3 0.2 0.2
64 2 0.1 0.1
65 9 0.5 0.5
66 1 0.1 0.1
67 3 0.2 0.2
68 5 0.3 0.3
69 3 0.2 0.2
7 2 0.1 0.1
70 2 0.1 0.1
71 3 0.2 0.2
72 2 0.1 0.1
73 3 0.2 0.2
74 0 0.0 0.0
75 0 0.0 0.0
77 1 0.1 0.1
78 0 0.0 0.0
79 0 0.0 0.0
8 2 0.1 0.1
80 0 0.0 0.0
81 0 0.0 0.0
9 5 0.3 0.3
93 0 0.0 0.0
97 0 0.0 0.0
“NA” 1553 88.5 88.5
Total 1754 100.0 100.0
Stomach ulcers
n % val%
No 1536 87.6 90.8
Yes 156 8.9 9.2
Scantron_Error 0 0.0 0.0
NA 62 3.5 NA
Total 1754 100.0 100.0
Stomach ulcers age
n % val%
0 20 1.1 1.1
10 1 0.1 0.1
13 0 0.0 0.0
14 2 0.1 0.1
15 1 0.1 0.1
16 4 0.2 0.2
17 0 0.0 0.0
18 2 0.1 0.1
19 0 0.0 0.0
2 1 0.1 0.1
20 3 0.2 0.2
21 0 0.0 0.0
22 2 0.1 0.1
23 2 0.1 0.1
24 2 0.1 0.1
25 4 0.2 0.2
27 2 0.1 0.1
28 2 0.1 0.1
3 0 0.0 0.0
30 6 0.3 0.3
32 2 0.1 0.1
34 1 0.1 0.1
35 8 0.5 0.5
36 2 0.1 0.1
38 0 0.0 0.0
39 2 0.1 0.1
40 12 0.7 0.7
42 0 0.0 0.0
44 1 0.1 0.1
45 8 0.5 0.5
46 0 0.0 0.0
47 3 0.2 0.2
48 4 0.2 0.2
49 1 0.1 0.1
50 6 0.3 0.3
52 1 0.1 0.1
53 1 0.1 0.1
54 0 0.0 0.0
55 3 0.2 0.2
56 2 0.1 0.1
57 2 0.1 0.1
58 2 0.1 0.1
59 1 0.1 0.1
60 2 0.1 0.1
61 1 0.1 0.1
62 7 0.4 0.4
63 3 0.2 0.2
64 2 0.1 0.1
65 2 0.1 0.1
66 1 0.1 0.1
67 4 0.2 0.2
68 1 0.1 0.1
69 2 0.1 0.1
7 0 0.0 0.0
70 1 0.1 0.1
71 1 0.1 0.1
72 2 0.1 0.1
73 0 0.0 0.0
74 1 0.1 0.1
76 1 0.1 0.1
8 1 0.1 0.1
80 0 0.0 0.0
9 1 0.1 0.1
94 1 0.1 0.1
“NA” 1604 91.4 91.4
Total 1754 100.0 100.0
Crohn’s Disease
n % val%
No 1629 92.9 96.4
Yes 60 3.4 3.6
Scantron_Error 0 0.0 0.0
NA 65 3.7 NA
Total 1754 100.0 100.0
Crohn’s Disease age
n % val%
15 0 0.0 0.0
2 1 0.1 0.1
22 0 0.0 0.0
30 1 0.1 0.1
32 1 0.1 0.1
33 1 0.1 0.1
34 0 0.0 0.0
35 1 0.1 0.1
37 1 0.1 0.1
39 1 0.1 0.1
40 6 0.3 0.3
41 0 0.0 0.0
44 2 0.1 0.1
45 1 0.1 0.1
46 1 0.1 0.1
48 1 0.1 0.1
50 2 0.1 0.1
51 1 0.1 0.1
52 2 0.1 0.1
53 1 0.1 0.1
54 2 0.1 0.1
55 3 0.2 0.2
56 2 0.1 0.1
57 0 0.0 0.0
58 2 0.1 0.1
59 1 0.1 0.1
60 4 0.2 0.2
62 1 0.1 0.1
63 2 0.1 0.1
64 0 0.0 0.0
65 1 0.1 0.1
66 1 0.1 0.1
67 0 0.0 0.0
69 2 0.1 0.1
70 1 0.1 0.1
71 1 0.1 0.1
74 1 0.1 0.1
75 1 0.1 0.1
76 1 0.1 0.1
79 0 0.0 0.0
85 1 0.1 0.1
“NA” 1703 97.1 97.1
Total 1754 100.0 100.0
Diabetes
n % val%
No 1173 66.9 68.6
Yes 534 30.4 31.2
Scantron_Error 3 0.2 0.2
NA 44 2.5 NA
Total 1754 100.0 100.0
Diabetes age
n % val%
0 5 0.3 0.3
10 1 0.1 0.1
12 1 0.1 0.1
13 2 0.1 0.1
14 1 0.1 0.1
15 2 0.1 0.1
16 1 0.1 0.1
17 1 0.1 0.1
18 0 0.0 0.0
19 0 0.0 0.0
2 1 0.1 0.1
20 1 0.1 0.1
22 0 0.0 0.0
24 1 0.1 0.1
27 1 0.1 0.1
28 1 0.1 0.1
29 1 0.1 0.1
3 0 0.0 0.0
30 7 0.4 0.4
31 1 0.1 0.1
32 1 0.1 0.1
34 1 0.1 0.1
35 13 0.7 0.7
36 0 0.0 0.0
37 3 0.2 0.2
38 2 0.1 0.1
39 3 0.2 0.2
40 23 1.3 1.3
41 2 0.1 0.1
42 5 0.3 0.3
43 7 0.4 0.4
44 6 0.3 0.3
45 26 1.5 1.5
46 6 0.3 0.3
47 7 0.4 0.4
48 6 0.3 0.3
49 3 0.2 0.2
5 1 0.1 0.1
50 43 2.5 2.5
51 5 0.3 0.3
52 8 0.5 0.5
53 11 0.6 0.6
54 16 0.9 0.9
55 36 2.1 2.1
56 13 0.7 0.7
57 8 0.5 0.5
58 15 0.9 0.9
59 15 0.9 0.9
60 34 1.9 1.9
61 13 0.7 0.7
62 17 1.0 1.0
63 9 0.5 0.5
64 10 0.6 0.6
65 18 1.0 1.0
66 5 0.3 0.3
67 4 0.2 0.2
68 6 0.3 0.3
69 7 0.4 0.4
70 12 0.7 0.7
71 4 0.2 0.2
72 3 0.2 0.2
73 1 0.1 0.1
74 2 0.1 0.1
75 2 0.1 0.1
76 0 0.0 0.0
78 1 0.1 0.1
81 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
97 1 0.1 0.1
“NA” 1292 73.7 73.7
Total 1754 100.0 100.0
Kidney Problems
n % val%
No 1597 91.0 93.7
Yes 107 6.1 6.3
Scantron_Error 1 0.1 0.1
NA 49 2.8 NA
Total 1754 100.0 100.0
Kidney Problems age
n % val%
11 0 0.0 0.0
15 1 0.1 0.1
16 1 0.1 0.1
17 1 0.1 0.1
29 0 0.0 0.0
35 1 0.1 0.1
38 0 0.0 0.0
40 3 0.2 0.2
42 1 0.1 0.1
44 0 0.0 0.0
45 1 0.1 0.1
46 1 0.1 0.1
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 4 0.2 0.2
51 2 0.1 0.1
52 1 0.1 0.1
53 2 0.1 0.1
54 5 0.3 0.3
55 5 0.3 0.3
56 6 0.3 0.3
57 1 0.1 0.1
58 1 0.1 0.1
59 2 0.1 0.1
60 1 0.1 0.1
61 3 0.2 0.2
62 6 0.3 0.3
63 1 0.1 0.1
64 4 0.2 0.2
65 7 0.4 0.4
66 2 0.1 0.1
67 4 0.2 0.2
68 1 0.1 0.1
69 3 0.2 0.2
7 1 0.1 0.1
70 4 0.2 0.2
71 1 0.1 0.1
72 1 0.1 0.1
74 1 0.1 0.1
75 3 0.2 0.2
76 1 0.1 0.1
77 0 0.0 0.0
80 0 0.0 0.0
83 0 0.0 0.0
95 0 0.0 0.0
“NA” 1671 95.3 95.3
Total 1754 100.0 100.0
Cirrhosis, liver damage
n % val%
No 1666 95.0 98.6
Yes 24 1.4 1.4
Scantron_Error 0 0.0 0.0
NA 64 3.6 NA
Total 1754 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 0 0.0 0.0
21 0 0.0 0.0
39 1 0.1 0.1
40 1 0.1 0.1
42 1 0.1 0.1
45 2 0.1 0.1
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 1 0.1 0.1
53 0 0.0 0.0
54 1 0.1 0.1
55 2 0.1 0.1
58 0 0.0 0.0
60 3 0.2 0.2
61 0 0.0 0.0
63 1 0.1 0.1
64 1 0.1 0.1
65 2 0.1 0.1
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 1 0.1 0.1
74 0 0.0 0.0
77 0 0.0 0.0
“NA” 1737 99.0 99.0
Total 1754 100.0 100.0
Arthritis
n % val%
No 1495 85.2 88.1
Yes 202 11.5 11.9
Scantron_Error 0 0.0 0.0
NA 57 3.2 NA
Total 1754 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
2 1 0.1 0.1
20 2 0.1 0.1
21 0 0.0 0.0
22 1 0.1 0.1
23 0 0.0 0.0
24 1 0.1 0.1
25 3 0.2 0.2
28 0 0.0 0.0
30 4 0.2 0.2
32 0 0.0 0.0
33 1 0.1 0.1
35 2 0.1 0.1
36 1 0.1 0.1
38 0 0.0 0.0
39 1 0.1 0.1
4 1 0.1 0.1
40 9 0.5 0.5
41 2 0.1 0.1
42 2 0.1 0.1
43 3 0.2 0.2
44 0 0.0 0.0
45 5 0.3 0.3
46 1 0.1 0.1
47 4 0.2 0.2
48 1 0.1 0.1
49 1 0.1 0.1
50 17 1.0 1.0
51 3 0.2 0.2
52 0 0.0 0.0
53 2 0.1 0.1
54 1 0.1 0.1
55 18 1.0 1.0
56 3 0.2 0.2
57 2 0.1 0.1
58 7 0.4 0.4
59 0 0.0 0.0
60 18 1.0 1.0
61 3 0.2 0.2
62 4 0.2 0.2
63 2 0.1 0.1
64 2 0.1 0.1
65 12 0.7 0.7
66 2 0.1 0.1
67 5 0.3 0.3
68 4 0.2 0.2
69 2 0.1 0.1
7 1 0.1 0.1
70 5 0.3 0.3
71 2 0.1 0.1
72 1 0.1 0.1
73 1 0.1 0.1
74 0 0.0 0.0
75 2 0.1 0.1
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
“NA” 1589 90.6 90.6
Total 1754 100.0 100.0
Dementia
n % val%
No 1677 95.6 98.5
Yes 25 1.4 1.5
Scantron_Error 1 0.1 0.1
NA 51 2.9 NA
Total 1754 100.0 100.0
Dementia age
n % val%
10 1 0.1 0.1
29 0 0.0 0.0
53 0 0.0 0.0
57 0 0.0 0.0
60 0 0.0 0.0
62 1 0.1 0.1
63 0 0.0 0.0
64 1 0.1 0.1
65 1 0.1 0.1
66 1 0.1 0.1
67 1 0.1 0.1
69 0 0.0 0.0
70 5 0.3 0.3
72 1 0.1 0.1
73 2 0.1 0.1
74 1 0.1 0.1
75 1 0.1 0.1
80 0 0.0 0.0
“NA” 1738 99.1 99.1
Total 1754 100.0 100.0
Depression
n % val%
No 1471 83.9 86.8
Yes 223 12.7 13.2
Scantron_Error 1 0.1 0.1
NA 59 3.4 NA
Total 1754 100.0 100.0
Depression age
n % val%
1 1 0.1 0.1
14 0 0.0 0.0
16 0 0.0 0.0
17 1 0.1 0.1
18 1 0.1 0.1
19 2 0.1 0.1
20 1 0.1 0.1
21 3 0.2 0.2
22 0 0.0 0.0
24 0 0.0 0.0
25 1 0.1 0.1
26 2 0.1 0.1
28 3 0.2 0.2
29 1 0.1 0.1
30 1 0.1 0.1
32 3 0.2 0.2
33 0 0.0 0.0
34 0 0.0 0.0
35 4 0.2 0.2
36 3 0.2 0.2
37 3 0.2 0.2
38 1 0.1 0.1
39 0 0.0 0.0
4 1 0.1 0.1
40 8 0.5 0.5
41 1 0.1 0.1
42 1 0.1 0.1
43 3 0.2 0.2
44 1 0.1 0.1
45 7 0.4 0.4
46 2 0.1 0.1
47 4 0.2 0.2
48 4 0.2 0.2
49 5 0.3 0.3
50 14 0.8 0.8
51 1 0.1 0.1
52 4 0.2 0.2
53 3 0.2 0.2
54 2 0.1 0.1
55 12 0.7 0.7
56 5 0.3 0.3
57 3 0.2 0.2
58 6 0.3 0.3
59 4 0.2 0.2
6 0 0.0 0.0
60 8 0.5 0.5
61 3 0.2 0.2
62 3 0.2 0.2
63 3 0.2 0.2
64 3 0.2 0.2
65 8 0.5 0.5
66 3 0.2 0.2
67 3 0.2 0.2
68 5 0.3 0.3
69 1 0.1 0.1
7 1 0.1 0.1
70 2 0.1 0.1
72 1 0.1 0.1
73 1 0.1 0.1
74 0 0.0 0.0
75 0 0.0 0.0
76 1 0.1 0.1
8 1 0.1 0.1
91 1 0.1 0.1
96 1 0.1 0.1
98 1 0.1 0.1
“NA” 1582 90.2 90.2
Total 1754 100.0 100.0
AIDS
n % val%
No 1675 95.5 98.8
Yes 20 1.1 1.2
Scantron_Error 0 0.0 0.0
NA 59 3.4 NA
Total 1754 100.0 100.0
AIDS age
n % val%
0 21 1.2 1.2
25 1 0.1 0.1
30 0 0.0 0.0
33 0 0.0 0.0
36 1 0.1 0.1
38 1 0.1 0.1
39 1 0.1 0.1
40 2 0.1 0.1
45 0 0.0 0.0
48 0 0.0 0.0
49 1 0.1 0.1
50 1 0.1 0.1
51 1 0.1 0.1
55 0 0.0 0.0
59 1 0.1 0.1
60 1 0.1 0.1
63 0 0.0 0.0
65 0 0.0 0.0
9 0 0.0 0.0
90 1 0.1 0.1
“NA” 1721 98.1 98.1
Total 1754 100.0 100.0
Other Cancer
n % val%
No 1564 89.2 92.9
Yes 120 6.8 7.1
Scantron_Error 0 0.0 0.0
NA 70 4.0 NA
Total 1754 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 0 0.0 0.0
16 1 0.1 0.1
18 1 0.1 0.1
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 1 0.1 0.1
30 1 0.1 0.1
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 1 0.1 0.1
40 1 0.1 0.1
41 0 0.0 0.0
42 1 0.1 0.1
43 1 0.1 0.1
44 0 0.0 0.0
45 1 0.1 0.1
47 1 0.1 0.1
48 0 0.0 0.0
49 4 0.2 0.2
50 1 0.1 0.1
51 1 0.1 0.1
52 1 0.1 0.1
54 4 0.2 0.2
55 4 0.2 0.2
56 5 0.3 0.3
57 2 0.1 0.1
58 7 0.4 0.4
59 2 0.1 0.1
60 8 0.5 0.5
61 6 0.3 0.3
62 5 0.3 0.3
63 4 0.2 0.2
64 4 0.2 0.2
65 3 0.2 0.2
66 2 0.1 0.1
67 3 0.2 0.2
68 3 0.2 0.2
69 3 0.2 0.2
7 0 0.0 0.0
70 5 0.3 0.3
71 1 0.1 0.1
72 2 0.1 0.1
73 1 0.1 0.1
74 5 0.3 0.3
75 2 0.1 0.1
76 1 0.1 0.1
77 1 0.1 0.1
78 1 0.1 0.1
79 0 0.0 0.0
“NA” 1653 94.2 94.2
Total 1754 100.0 100.0

Michigan

Heart Attack
n % val%
No 14 87.5 87.5
Yes 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Heart Attack age
n % val%
0 0 0.0 0.0
1 0 0.0 0.0
14 0 0.0 0.0
17 0 0.0 0.0
20 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
29 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 6.2 6.2
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 1 6.2 6.2
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
79 0 0.0 0.0
93 0 0.0 0.0
“NA” 13 81.2 81.2
Total 16 100.0 100.0
Heart Failure or CHF
n % val%
No 13 81.2 81.2
Yes 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Heart Failure or CHF age
n % val%
12 0 0.0 0.0
13 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
20 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 1 6.2 6.2
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 1 6.2 6.2
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
80 0 0.0 0.0
82 0 0.0 0.0
97 0 0.0 0.0
“NA” 13 81.2 81.2
Total 16 100.0 100.0
Stroke
n % val%
No 13 81.2 86.7
Yes 2 12.5 13.3
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
Stroke age
n % val%
0 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
23 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
30 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 6.2 6.2
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 1 6.2 6.2
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
“NA” 14 87.5 87.5
Total 16 100.0 100.0
Hypertension
n % val%
No 4 25 25
Yes 12 75 75
Scantron_Error 0 0 0
Total 16 100 100
Hypertension age
n % val%
.35 0 0.0 0.0
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 1 6.2 6.2
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 1 6.2 6.2
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
41 2 12.5 12.5
42 0 0.0 0.0
43 0 0.0 0.0
44 1 6.2 6.2
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 1 6.2 6.2
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
6 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 1 6.2 6.2
64 0 0.0 0.0
65 1 6.2 6.2
66 1 6.2 6.2
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
89 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
99 0 0.0 0.0
“NA” 6 37.5 37.5
Total 16 100.0 100.0
Peripheral arterial disease
n % val%
No 14 87.5 93.3
Yes 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
Peripheral arterial disease age
n % val%
* 0 0.0 0.0
** 0 0.0 0.0
0 0 0.0 0.0
1 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
5 0 0.0 0.0
50 1 6.2 6.2
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
82 0 0.0 0.0
84 0 0.0 0.0
94 0 0.0 0.0
97 0 0.0 0.0
“NA” 15 93.8 93.8
Total 16 100.0 100.0
High Cholesterol
n % val%
No 7 43.8 46.7
Yes 8 50.0 53.3
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
High Cholesterol age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 1 6.2 6.2
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
41 1 6.2 6.2
42 0 0.0 0.0
43 1 6.2 6.2
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 1 6.2 6.2
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 2 12.5 12.5
58 0 0.0 0.0
59 0 0.0 0.0
6 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 1 6.2 6.2
64 0 0.0 0.0
65 1 6.2 6.2
66 1 6.2 6.2
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
86 0 0.0 0.0
9 0 0.0 0.0
92 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
96 0 0.0 0.0
97 0 0.0 0.0
99 0 0.0 0.0
“NA” 7 43.8 43.8
Total 16 100.0 100.0
Asthma, COPD
n % val%
No 11 68.8 73.3
Yes 4 25.0 26.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
Asthma, COPD age
n % val%
06 0 0.0 0.0
1 0 0.0 0.0
10 0 0.0 0.0
11 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
23 0 0.0 0.0
25 1 6.2 6.2
26 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 1 6.2 6.2
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
6 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 1 6.2 6.2
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
81 0 0.0 0.0
9 0 0.0 0.0
93 0 0.0 0.0
97 0 0.0 0.0
“NA” 12 75.0 75.0
Total 16 100.0 100.0
Stomach ulcers
n % val%
No 13 81.2 92.9
Yes 1 6.2 7.1
Scantron_Error 0 0.0 0.0
NA 2 12.5 NA
Total 16 100.0 100.0
Stomach ulcers age
n % val%
0 0 0.0 0.0
10 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
27 0 0.0 0.0
28 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 6.2 6.2
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
80 0 0.0 0.0
9 0 0.0 0.0
94 0 0.0 0.0
“NA” 15 93.8 93.8
Total 16 100.0 100.0
Crohn’s Disease
n % val%
No 14 87.5 100
Yes 0 0.0 0
Scantron_Error 0 0.0 0
NA 2 12.5 NA
Total 16 100.0 100
Crohn’s Disease age
n % val%
15 0 0 0
2 0 0 0
22 0 0 0
30 0 0 0
32 0 0 0
33 0 0 0
34 0 0 0
35 0 0 0
37 0 0 0
39 0 0 0
40 0 0 0
41 0 0 0
44 0 0 0
45 0 0 0
46 0 0 0
48 0 0 0
50 0 0 0
51 0 0 0
52 0 0 0
53 0 0 0
54 0 0 0
55 0 0 0
56 0 0 0
57 0 0 0
58 0 0 0
59 0 0 0
60 0 0 0
62 0 0 0
63 0 0 0
64 0 0 0
65 0 0 0
66 0 0 0
67 0 0 0
69 0 0 0
70 0 0 0
71 0 0 0
74 0 0 0
75 0 0 0
76 0 0 0
79 0 0 0
85 0 0 0
“NA” 16 100 100
Total 16 100 100
Diabetes
n % val%
No 7 43.8 43.8
Yes 9 56.2 56.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Diabetes age
n % val%
0 0 0.0 0.0
10 0 0.0 0.0
12 0 0.0 0.0
13 0 0.0 0.0
14 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
27 0 0.0 0.0
28 1 6.2 6.2
29 0 0.0 0.0
3 0 0.0 0.0
30 0 0.0 0.0
31 0 0.0 0.0
32 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 1 6.2 6.2
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 1 6.2 6.2
48 0 0.0 0.0
49 1 6.2 6.2
5 0 0.0 0.0
50 1 6.2 6.2
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 1 6.2 6.2
64 0 0.0 0.0
65 0 0.0 0.0
66 1 6.2 6.2
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 1 6.2 6.2
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
78 0 0.0 0.0
81 0 0.0 0.0
94 0 0.0 0.0
95 0 0.0 0.0
97 0 0.0 0.0
“NA” 7 43.8 43.8
Total 16 100.0 100.0
Kidney Problems
n % val%
No 13 81.2 81.2
Yes 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Kidney Problems age
n % val%
11 0 0.0 0.0
15 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
29 0 0.0 0.0
35 0 0.0 0.0
38 0 0.0 0.0
40 0 0.0 0.0
42 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
5 0 0.0 0.0
50 1 6.2 6.2
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 1 6.2 6.2
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
83 0 0.0 0.0
95 0 0.0 0.0
“NA” 14 87.5 87.5
Total 16 100.0 100.0
Cirrhosis, liver damage
n % val%
No 15 93.8 93.8
Yes 1 6.2 6.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Cirrhosis, liver damage age
n % val%
18 0 0 0
21 0 0 0
39 0 0 0
40 0 0 0
42 0 0 0
45 0 0 0
47 0 0 0
48 0 0 0
49 0 0 0
50 0 0 0
51 0 0 0
53 0 0 0
54 0 0 0
55 0 0 0
58 0 0 0
60 0 0 0
61 0 0 0
63 0 0 0
64 0 0 0
65 0 0 0
66 0 0 0
67 0 0 0
68 0 0 0
69 0 0 0
74 0 0 0
77 0 0 0
“NA” 16 100 100
Total 16 100 100
Arthritis
n % val%
No 14 87.5 87.5
Yes 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Arthritis age
n % val%
1 0 0.0 0.0
10 0 0.0 0.0
17 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
23 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
28 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 1 6.2 6.2
58 0 0.0 0.0
59 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
80 0 0.0 0.0
“NA” 15 93.8 93.8
Total 16 100.0 100.0
Dementia
n % val%
No 16 100 100
Yes 0 0 0
Scantron_Error 0 0 0
Total 16 100 100
Dementia age
n % val%
10 0 0 0
29 0 0 0
53 0 0 0
57 0 0 0
60 0 0 0
62 0 0 0
63 0 0 0
64 0 0 0
65 0 0 0
66 0 0 0
67 0 0 0
69 0 0 0
70 0 0 0
72 0 0 0
73 0 0 0
74 0 0 0
75 0 0 0
80 0 0 0
“NA” 16 100 100
Total 16 100 100
Depression
n % val%
No 14 87.5 87.5
Yes 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Depression age
n % val%
1 0 0.0 0.0
14 0 0.0 0.0
16 0 0.0 0.0
17 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
20 0 0.0 0.0
21 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
25 0 0.0 0.0
26 0 0.0 0.0
28 0 0.0 0.0
29 0 0.0 0.0
30 0 0.0 0.0
32 0 0.0 0.0
33 0 0.0 0.0
34 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
37 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
4 0 0.0 0.0
40 1 6.2 6.2
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
46 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
53 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
6 0 0.0 0.0
60 0 0.0 0.0
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
8 0 0.0 0.0
91 0 0.0 0.0
96 0 0.0 0.0
98 0 0.0 0.0
“NA” 15 93.8 93.8
Total 16 100.0 100.0
AIDS
n % val%
No 16 100 100
Yes 0 0 0
Scantron_Error 0 0 0
Total 16 100 100
AIDS age
n % val%
0 0 0 0
25 0 0 0
30 0 0 0
33 0 0 0
36 0 0 0
38 0 0 0
39 0 0 0
40 0 0 0
45 0 0 0
48 0 0 0
49 0 0 0
50 0 0 0
51 0 0 0
55 0 0 0
59 0 0 0
60 0 0 0
63 0 0 0
65 0 0 0
9 0 0 0
90 0 0 0
“NA” 16 100 100
Total 16 100 100
Other Cancer
n % val%
No 15 93.8 93.8
Yes 1 6.2 6.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
Other Cancer age
n % val%
10 0 0.0 0.0
12 0 0.0 0.0
16 0 0.0 0.0
18 0 0.0 0.0
19 0 0.0 0.0
2 0 0.0 0.0
20 0 0.0 0.0
22 0 0.0 0.0
24 0 0.0 0.0
30 0 0.0 0.0
35 0 0.0 0.0
36 0 0.0 0.0
38 0 0.0 0.0
39 0 0.0 0.0
40 0 0.0 0.0
41 0 0.0 0.0
42 0 0.0 0.0
43 0 0.0 0.0
44 0 0.0 0.0
45 0 0.0 0.0
47 0 0.0 0.0
48 0 0.0 0.0
49 0 0.0 0.0
50 0 0.0 0.0
51 0 0.0 0.0
52 0 0.0 0.0
54 0 0.0 0.0
55 0 0.0 0.0
56 0 0.0 0.0
57 0 0.0 0.0
58 0 0.0 0.0
59 0 0.0 0.0
60 1 6.2 6.2
61 0 0.0 0.0
62 0 0.0 0.0
63 0 0.0 0.0
64 0 0.0 0.0
65 0 0.0 0.0
66 0 0.0 0.0
67 0 0.0 0.0
68 0 0.0 0.0
69 0 0.0 0.0
7 0 0.0 0.0
70 0 0.0 0.0
71 0 0.0 0.0
72 0 0.0 0.0
73 0 0.0 0.0
74 0 0.0 0.0
75 0 0.0 0.0
76 0 0.0 0.0
77 0 0.0 0.0
78 0 0.0 0.0
79 0 0.0 0.0
“NA” 15 93.8 93.8
Total 16 100.0 100.0

B5

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location

All data

  b5 <- as.factor(d[,"b5"])
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6",
                     Scantron_Error="*")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 246 6.9 7.2
Hospital_urgent_care_clinic 263 7.4 7.7
Private_Dr_office 2277 64.0 66.7
ER 24 0.7 0.7
VA 361 10.1 10.6
Other 63 1.8 1.8
Scantron_Error 178 5.0 5.2
NA 145 4.1 NA
Total 3557 100.0 100.0

LA County

n % val%
Community_center_free_clinic 21 6.5 6.6
Hospital_urgent_care_clinic 20 6.2 6.3
Private_Dr_office 242 75.4 76.6
ER 2 0.6 0.6
VA 13 4.0 4.1
Other 6 1.9 1.9
Scantron_Error 12 3.7 3.8
NA 5 1.6 NA
Total 321 100.0 100.0

Northern CA

n % val%
Community_center_free_clinic 16 7.6 8.0
Hospital_urgent_care_clinic 22 10.5 10.9
Private_Dr_office 145 69.0 72.1
ER 1 0.5 0.5
VA 7 3.3 3.5
Other 3 1.4 1.5
Scantron_Error 7 3.3 3.5
NA 9 4.3 NA
Total 210 100.0 100.0

Greater CA

n % val%
Community_center_free_clinic 23 7.3 7.7
Hospital_urgent_care_clinic 27 8.6 9.0
Private_Dr_office 216 68.6 72.2
ER 2 0.6 0.7
VA 18 5.7 6.0
Other 7 2.2 2.3
Scantron_Error 6 1.9 2.0
NA 16 5.1 NA
Total 315 100.0 100.0

Detroit

n % val%
Community_center_free_clinic 25 7.0 7.4
Hospital_urgent_care_clinic 46 12.9 13.6
Private_Dr_office 231 64.9 68.1
ER 2 0.6 0.6
VA 10 2.8 2.9
Other 9 2.5 2.7
Scantron_Error 16 4.5 4.7
NA 17 4.8 NA
Total 356 100.0 100.0

Louisiana

n % val%
Community_center_free_clinic 59 10.1 10.5
Hospital_urgent_care_clinic 58 9.9 10.4
Private_Dr_office 315 53.8 56.2
ER 4 0.7 0.7
VA 76 13.0 13.6
Other 6 1.0 1.1
Scantron_Error 42 7.2 7.5
NA 25 4.3 NA
Total 585 100.0 100.0

Georgia

n % val%
Community_center_free_clinic 101 5.8 6.0
Hospital_urgent_care_clinic 87 5.0 5.2
Private_Dr_office 1119 63.8 66.5
ER 13 0.7 0.8
VA 237 13.5 14.1
Other 32 1.8 1.9
Scantron_Error 94 5.4 5.6
NA 71 4.0 NA
Total 1754 100.0 100.0

Michigan

n % val%
Community_center_free_clinic 1 6.2 7.1
Hospital_urgent_care_clinic 3 18.8 21.4
Private_Dr_office 9 56.2 64.3
ER 0 0.0 0.0
VA 0 0.0 0.0
Other 0 0.0 0.0
Scantron_Error 1 6.2 7.1
NA 2 12.5 NA
Total 16 100.0 100.0

C1

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years

All data

  c1 <- as.factor(d[,"c1"])
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6",
                     Scantron_Error="*")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 137 3.9 3.9 3.9 3.9
years_1_5 610 17.1 17.4 21.0 21.3
years_6_10 478 13.4 13.6 34.4 34.9
years_11_15 413 11.6 11.8 46.1 46.7
years_16_20 473 13.3 13.5 59.3 60.2
years_21_more 1392 39.1 39.7 98.5 99.9
Scantron_Error 5 0.1 0.1 98.6 100.0
NA 49 1.4 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

n % val% %cum val%cum
Less_than_1_year 18 5.6 5.6 5.6 5.6
years_1_5 67 20.9 20.9 26.5 26.5
years_6_10 39 12.1 12.1 38.6 38.6
years_11_15 26 8.1 8.1 46.7 46.7
years_16_20 40 12.5 12.5 59.2 59.2
years_21_more 131 40.8 40.8 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 321 100.0 100.0 100.0 100.0

Northern CA

n % val% %cum val%cum
Less_than_1_year 12 5.7 5.8 5.7 5.8
years_1_5 36 17.1 17.5 22.9 23.3
years_6_10 29 13.8 14.1 36.7 37.4
years_11_15 25 11.9 12.1 48.6 49.5
years_16_20 21 10.0 10.2 58.6 59.7
years_21_more 82 39.0 39.8 97.6 99.5
Scantron_Error 1 0.5 0.5 98.1 100.0
NA 4 1.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

n % val% %cum val%cum
Less_than_1_year 11 3.5 3.6 3.5 3.6
years_1_5 74 23.5 24.1 27.0 27.7
years_6_10 53 16.8 17.3 43.8 45.0
years_11_15 43 13.7 14.0 57.5 59.0
years_16_20 46 14.6 15.0 72.1 73.9
years_21_more 79 25.1 25.7 97.1 99.7
Scantron_Error 1 0.3 0.3 97.5 100.0
NA 8 2.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

n % val% %cum val%cum
Less_than_1_year 18 5.1 5.1 5.1 5.1
years_1_5 67 18.8 18.9 23.9 23.9
years_6_10 57 16.0 16.1 39.9 40.0
years_11_15 48 13.5 13.5 53.4 53.5
years_16_20 38 10.7 10.7 64.0 64.2
years_21_more 126 35.4 35.5 99.4 99.7
Scantron_Error 1 0.3 0.3 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

n % val% %cum val%cum
Less_than_1_year 16 2.7 2.8 2.7 2.8
years_1_5 75 12.8 13.0 15.6 15.8
years_6_10 69 11.8 12.0 27.4 27.7
years_11_15 72 12.3 12.5 39.7 40.2
years_16_20 60 10.3 10.4 49.9 50.6
years_21_more 284 48.5 49.2 98.5 99.8
Scantron_Error 1 0.2 0.2 98.6 100.0
NA 8 1.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

n % val% %cum val%cum
Less_than_1_year 62 3.5 3.6 3.5 3.6
years_1_5 289 16.5 16.7 20.0 20.3
years_6_10 227 12.9 13.2 33.0 33.5
years_11_15 196 11.2 11.4 44.1 44.8
years_16_20 266 15.2 15.4 59.3 60.3
years_21_more 685 39.1 39.7 98.3 99.9
Scantron_Error 1 0.1 0.1 98.4 100.0
NA 28 1.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

n % val% %cum val%cum
Less_than_1_year 0 0.0 0.0 0.0 0.0
years_1_5 2 12.5 12.5 12.5 12.5
years_6_10 4 25.0 25.0 37.5 37.5
years_11_15 3 18.8 18.8 56.2 56.2
years_16_20 2 12.5 12.5 68.8 68.8
years_21_more 5 31.2 31.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0

C2A

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree

All data

  c2a1 <- as.factor(d[,"c2a1"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 1637 46.0 47.0 46.0 47.0
Agree 1174 33.0 33.7 79.0 80.8
Neutral 431 12.1 12.4 91.1 93.2
Disagree 176 4.9 5.1 96.1 98.2
Strongly_Disagree 56 1.6 1.6 97.7 99.8
Scantron_Error 6 0.2 0.2 97.8 100.0
NA 77 2.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 114 35.5 36.4 35.5 36.4
Agree 131 40.8 41.9 76.3 78.3
Neutral 45 14.0 14.4 90.3 92.7
Disagree 20 6.2 6.4 96.6 99.0
Strongly_Disagree 3 0.9 1.0 97.5 100.0
Scantron_Error 0 0.0 0.0 97.5 100.0
NA 8 2.5 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 109 51.9 52.7 51.9 52.7
Agree 60 28.6 29.0 80.5 81.6
Neutral 29 13.8 14.0 94.3 95.7
Disagree 9 4.3 4.3 98.6 100.0
Strongly_Disagree 0 0.0 0.0 98.6 100.0
Scantron_Error 0 0.0 0.0 98.6 100.0
NA 3 1.4 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 158 50.2 51.6 50.2 51.6
Agree 98 31.1 32.0 81.3 83.7
Neutral 28 8.9 9.2 90.2 92.8
Disagree 16 5.1 5.2 95.2 98.0
Strongly_Disagree 5 1.6 1.6 96.8 99.7
Scantron_Error 1 0.3 0.3 97.1 100.0
NA 9 2.9 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 130 36.5 37.2 36.5 37.2
Agree 117 32.9 33.5 69.4 70.8
Neutral 68 19.1 19.5 88.5 90.3
Disagree 21 5.9 6.0 94.4 96.3
Strongly_Disagree 13 3.7 3.7 98.0 100.0
Scantron_Error 0 0.0 0.0 98.0 100.0
NA 7 2.0 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 252 43.1 44.0 43.1 44.0
Agree 196 33.5 34.2 76.6 78.2
Neutral 77 13.2 13.4 89.7 91.6
Disagree 35 6.0 6.1 95.7 97.7
Strongly_Disagree 12 2.1 2.1 97.8 99.8
Scantron_Error 1 0.2 0.2 97.9 100.0
NA 12 2.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 869 49.5 50.6 49.5 50.6
Agree 563 32.1 32.8 81.6 83.4
Neutral 183 10.4 10.7 92.1 94.1
Disagree 75 4.3 4.4 96.4 98.5
Strongly_Disagree 22 1.3 1.3 97.6 99.8
Scantron_Error 4 0.2 0.2 97.8 100.0
NA 38 2.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 5 31.2 31.2 31.2 31.2
Agree 9 56.2 56.2 87.5 87.5
Neutral 1 6.2 6.2 93.8 93.8
Disagree 0 0.0 0.0 93.8 93.8
Strongly_Disagree 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100

C2B

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree

All data

  c2b1 <- as.factor(d[,"c2b1"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 1399 39.3 40.6 39.3 40.6
Agree 1094 30.8 31.7 70.1 72.3
Neutral 523 14.7 15.2 84.8 87.5
Disagree 299 8.4 8.7 93.2 96.1
Strongly_Disagree 129 3.6 3.7 96.8 99.9
Scantron_Error 4 0.1 0.1 96.9 100.0
NA 109 3.1 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 89 27.7 28.6 27.7 28.6
Agree 106 33.0 34.1 60.7 62.7
Neutral 63 19.6 20.3 80.4 83.0
Disagree 42 13.1 13.5 93.5 96.5
Strongly_Disagree 11 3.4 3.5 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 10 3.1 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 92 43.8 44.4 43.8 44.4
Agree 55 26.2 26.6 70.0 71.0
Neutral 38 18.1 18.4 88.1 89.4
Disagree 14 6.7 6.8 94.8 96.1
Strongly_Disagree 8 3.8 3.9 98.6 100.0
Scantron_Error 0 0.0 0.0 98.6 100.0
NA 3 1.4 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 140 44.4 46.1 44.4 46.1
Agree 92 29.2 30.3 73.7 76.3
Neutral 39 12.4 12.8 86.0 89.1
Disagree 23 7.3 7.6 93.3 96.7
Strongly_Disagree 10 3.2 3.3 96.5 100.0
Scantron_Error 0 0.0 0.0 96.5 100.0
NA 11 3.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 96 27.0 28.0 27.0 28.0
Agree 93 26.1 27.1 53.1 55.1
Neutral 96 27.0 28.0 80.1 83.1
Disagree 39 11.0 11.4 91.0 94.5
Strongly_Disagree 19 5.3 5.5 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 13 3.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 194 33.2 34.5 33.2 34.5
Agree 192 32.8 34.1 66.0 68.6
Neutral 76 13.0 13.5 79.0 82.1
Disagree 69 11.8 12.3 90.8 94.3
Strongly_Disagree 30 5.1 5.3 95.9 99.6
Scantron_Error 2 0.3 0.4 96.2 100.0
NA 22 3.8 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 783 44.6 46.0 44.6 46.0
Agree 551 31.4 32.3 76.1 78.3
Neutral 209 11.9 12.3 88.0 90.6
Disagree 109 6.2 6.4 94.2 96.9
Strongly_Disagree 50 2.9 2.9 97.0 99.9
Scantron_Error 2 0.1 0.1 97.1 100.0
NA 50 2.9 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 5 31.2 31.2 31.2 31.2
Agree 5 31.2 31.2 62.5 62.5
Neutral 2 12.5 12.5 75.0 75.0
Disagree 3 18.8 18.8 93.8 93.8
Strongly_Disagree 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100

C2C

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree

All data

  c2c1 <- as.factor(d[,"c2c1"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 1026 28.8 29.9 28.8 29.9
Agree 1073 30.2 31.2 59.0 61.1
Neutral 772 21.7 22.5 80.7 83.6
Disagree 436 12.3 12.7 93.0 96.3
Strongly_Disagree 123 3.5 3.6 96.4 99.9
Scantron_Error 5 0.1 0.1 96.6 100.0
NA 122 3.4 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 3557 100 NA 100 NA
Total 3557 100 100 100 100

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 68 21.2 21.9 21.2 21.9
Agree 89 27.7 28.6 48.9 50.5
Neutral 81 25.2 26.0 74.1 76.5
Disagree 62 19.3 19.9 93.5 96.5
Strongly_Disagree 11 3.4 3.5 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 10 3.1 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 63 30.0 30.9 30.0 30.9
Agree 59 28.1 28.9 58.1 59.8
Neutral 46 21.9 22.5 80.0 82.4
Disagree 28 13.3 13.7 93.3 96.1
Strongly_Disagree 8 3.8 3.9 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 6 2.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 84 26.7 27.8 26.7 27.8
Agree 102 32.4 33.8 59.0 61.6
Neutral 76 24.1 25.2 83.2 86.8
Disagree 32 10.2 10.6 93.3 97.4
Strongly_Disagree 8 2.5 2.6 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 69 19.4 20.0 19.4 20.0
Agree 78 21.9 22.6 41.3 42.6
Neutral 112 31.5 32.5 72.8 75.1
Disagree 63 17.7 18.3 90.4 93.3
Strongly_Disagree 23 6.5 6.7 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 11 3.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 156 26.7 27.6 26.7 27.6
Agree 180 30.8 31.9 57.4 59.5
Neutral 122 20.9 21.6 78.3 81.1
Disagree 77 13.2 13.6 91.5 94.7
Strongly_Disagree 28 4.8 5.0 96.2 99.6
Scantron_Error 2 0.3 0.4 96.6 100.0
NA 20 3.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 580 33.1 34.3 33.1 34.3
Agree 561 32.0 33.2 65.1 67.4
Neutral 330 18.8 19.5 83.9 86.9
Disagree 173 9.9 10.2 93.7 97.2
Strongly_Disagree 45 2.6 2.7 96.3 99.8
Scantron_Error 3 0.2 0.2 96.5 100.0
NA 62 3.5 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 1754 100 NA 100 NA
Total 1754 100 100 100 100

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 6 37.5 37.5 37.5 37.5
Agree 4 25.0 25.0 62.5 62.5
Neutral 5 31.2 31.2 93.8 93.8
Disagree 1 6.2 6.2 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 0 0 NaN 0 NaN
Agree 0 0 NaN 0 NaN
Neutral 0 0 NaN 0 NaN
Disagree 0 0 NaN 0 NaN
Strongly_Disagree 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 16 100 NA 100 NA
Total 16 100 100 100 100

C3A

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know

All data

  c3a1 <- as.factor(d[,"c3a1"])
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 2430 68.3 70.3 68.3 70.3
Somewhat_serious 642 18.0 18.6 86.4 88.8
Very_serious 245 6.9 7.1 93.3 95.9
Dont_know 139 3.9 4.0 97.2 99.9
Scantron_Error 3 0.1 0.1 97.2 100.0
NA 98 2.8 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 2310 64.9 69.3 64.9 69.3
Somewhat_serious 705 19.8 21.1 84.8 90.4
Very_serious 141 4.0 4.2 88.7 94.6
Dont_know 176 4.9 5.3 93.7 99.9
Scantron_Error 3 0.1 0.1 93.8 100.0
NA 222 6.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 2551 71.7 77.2 71.7 77.2
Somewhat_serious 397 11.2 12.0 82.9 89.2
Very_serious 90 2.5 2.7 85.4 91.9
Dont_know 264 7.4 8.0 92.8 99.9
Scantron_Error 3 0.1 0.1 92.9 100.0
NA 252 7.1 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 161 50.2 51.1 50.2 51.1
Somewhat_serious 96 29.9 30.5 80.1 81.6
Very_serious 53 16.5 16.8 96.6 98.4
Dont_know 5 1.6 1.6 98.1 100.0
Scantron_Error 0 0.0 0.0 98.1 100.0
NA 6 1.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 163 50.8 52.2 50.8 52.2
Somewhat_serious 116 36.1 37.2 86.9 89.4
Very_serious 24 7.5 7.7 94.4 97.1
Dont_know 9 2.8 2.9 97.2 100.0
Scantron_Error 0 0.0 0.0 97.2 100.0
NA 9 2.8 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 235 73.2 75.8 73.2 75.8
Somewhat_serious 44 13.7 14.2 86.9 90.0
Very_serious 14 4.4 4.5 91.3 94.5
Dont_know 17 5.3 5.5 96.6 100.0
Scantron_Error 0 0.0 0.0 96.6 100.0
NA 11 3.4 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 124 59.0 60.5 59.0 60.5
Somewhat_serious 59 28.1 28.8 87.1 89.3
Very_serious 19 9.0 9.3 96.2 98.5
Dont_know 3 1.4 1.5 97.6 100.0
Scantron_Error 0 0.0 0.0 97.6 100.0
NA 5 2.4 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 128 61.0 63.4 61.0 63.4
Somewhat_serious 54 25.7 26.7 86.7 90.1
Very_serious 13 6.2 6.4 92.9 96.5
Dont_know 7 3.3 3.5 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 8 3.8 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 149 71.0 74.9 71.0 74.9
Somewhat_serious 30 14.3 15.1 85.2 89.9
Very_serious 6 2.9 3.0 88.1 93.0
Dont_know 14 6.7 7.0 94.8 100.0
Scantron_Error 0 0.0 0.0 94.8 100.0
NA 11 5.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 217 68.9 71.1 68.9 71.1
Somewhat_serious 60 19.0 19.7 87.9 90.8
Very_serious 21 6.7 6.9 94.6 97.7
Dont_know 7 2.2 2.3 96.8 100.0
Scantron_Error 0 0.0 0.0 96.8 100.0
NA 10 3.2 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 195 61.9 65.4 61.9 65.4
Somewhat_serious 66 21.0 22.1 82.9 87.6
Very_serious 22 7.0 7.4 89.8 95.0
Dont_know 15 4.8 5.0 94.6 100.0
Scantron_Error 0 0.0 0.0 94.6 100.0
NA 17 5.4 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 208 66.0 70.0 66.0 70.0
Somewhat_serious 53 16.8 17.8 82.9 87.9
Very_serious 15 4.8 5.1 87.6 92.9
Dont_know 21 6.7 7.1 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 18 5.7 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 238 66.9 69.6 66.9 69.6
Somewhat_serious 67 18.8 19.6 85.7 89.2
Very_serious 19 5.3 5.6 91.0 94.7
Dont_know 18 5.1 5.3 96.1 100.0
Scantron_Error 0 0.0 0.0 96.1 100.0
NA 14 3.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 219 61.5 67.6 61.5 67.6
Somewhat_serious 82 23.0 25.3 84.6 92.9
Very_serious 8 2.2 2.5 86.8 95.4
Dont_know 15 4.2 4.6 91.0 100.0
Scantron_Error 0 0.0 0.0 91.0 100.0
NA 32 9.0 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 238 66.9 74.1 66.9 74.1
Somewhat_serious 48 13.5 15.0 80.3 89.1
Very_serious 7 2.0 2.2 82.3 91.3
Dont_know 28 7.9 8.7 90.2 100.0
Scantron_Error 0 0.0 0.0 90.2 100.0
NA 35 9.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 434 74.2 76.1 74.2 76.1
Somewhat_serious 75 12.8 13.2 87.0 89.3
Very_serious 26 4.4 4.6 91.5 93.9
Dont_know 34 5.8 6.0 97.3 99.8
Scantron_Error 1 0.2 0.2 97.4 100.0
NA 15 2.6 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 400 68.4 74.3 68.4 74.3
Somewhat_serious 91 15.6 16.9 83.9 91.3
Very_serious 15 2.6 2.8 86.5 94.1
Dont_know 31 5.3 5.8 91.8 99.8
Scantron_Error 1 0.2 0.2 92.0 100.0
NA 47 8.0 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 422 72.1 78.7 72.1 78.7
Somewhat_serious 54 9.2 10.1 81.4 88.8
Very_serious 14 2.4 2.6 83.8 91.4
Dont_know 45 7.7 8.4 91.5 99.8
Scantron_Error 1 0.2 0.2 91.6 100.0
NA 49 8.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1245 71.0 73.0 71.0 73.0
Somewhat_serious 281 16.0 16.5 87.0 89.4
Very_serious 107 6.1 6.3 93.1 95.7
Dont_know 71 4.0 4.2 97.1 99.9
Scantron_Error 2 0.1 0.1 97.3 100.0
NA 48 2.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1194 68.1 72.5 68.1 72.5
Somewhat_serious 294 16.8 17.9 84.8 90.4
Very_serious 59 3.4 3.6 88.2 94.0
Dont_know 97 5.5 5.9 93.7 99.9
Scantron_Error 2 0.1 0.1 93.8 100.0
NA 108 6.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1287 73.4 79.1 73.4 79.1
Somewhat_serious 167 9.5 10.3 82.9 89.4
Very_serious 34 1.9 2.1 84.8 91.5
Dont_know 137 7.8 8.4 92.6 99.9
Scantron_Error 2 0.1 0.1 92.8 100.0
NA 127 7.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 11 68.8 68.8 68.8 68.8
Somewhat_serious 4 25.0 25.0 93.8 93.8
Very_serious 0 0.0 0.0 93.8 93.8
Dont_know 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 11 68.8 73.3 68.8 73.3
Somewhat_serious 2 12.5 13.3 81.2 86.7
Very_serious 0 0.0 0.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 12 75.0 80.0 75.0 80.0
Somewhat_serious 1 6.2 6.7 81.2 86.7
Very_serious 0 0.0 0.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C3B

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know

All data

  c3b1 <- as.factor(d[,"c3b1"])
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 2816 79.2 82.1 79.2 82.1
Somewhat_serious 435 12.2 12.7 91.4 94.8
Very_serious 99 2.8 2.9 94.2 97.6
Dont_know 79 2.2 2.3 96.4 99.9
Scantron_Error 2 0.1 0.1 96.5 100.0
NA 126 3.5 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 2541 71.4 77.0 71.4 77.0
Somewhat_serious 538 15.1 16.3 86.6 93.3
Very_serious 94 2.6 2.8 89.2 96.2
Dont_know 124 3.5 3.8 92.7 99.9
Scantron_Error 3 0.1 0.1 92.8 100.0
NA 257 7.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 2531 71.2 77.1 71.2 77.1
Somewhat_serious 437 12.3 13.3 83.4 90.4
Very_serious 107 3.0 3.3 86.4 93.6
Dont_know 207 5.8 6.3 92.3 99.9
Scantron_Error 2 0.1 0.1 92.3 100.0
NA 273 7.7 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 229 71.3 73.4 71.3 73.4
Somewhat_serious 61 19.0 19.6 90.3 92.9
Very_serious 17 5.3 5.4 95.6 98.4
Dont_know 5 1.6 1.6 97.2 100.0
Scantron_Error 0 0.0 0.0 97.2 100.0
NA 9 2.8 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 213 66.4 68.7 66.4 68.7
Somewhat_serious 73 22.7 23.5 89.1 92.3
Very_serious 14 4.4 4.5 93.5 96.8
Dont_know 10 3.1 3.2 96.6 100.0
Scantron_Error 0 0.0 0.0 96.6 100.0
NA 11 3.4 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 235 73.2 76.1 73.2 76.1
Somewhat_serious 45 14.0 14.6 87.2 90.6
Very_serious 12 3.7 3.9 91.0 94.5
Dont_know 17 5.3 5.5 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 12 3.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 158 75.2 77.5 75.2 77.5
Somewhat_serious 37 17.6 18.1 92.9 95.6
Very_serious 8 3.8 3.9 96.7 99.5
Dont_know 1 0.5 0.5 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 6 2.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 145 69.0 72.1 69.0 72.1
Somewhat_serious 39 18.6 19.4 87.6 91.5
Very_serious 11 5.2 5.5 92.9 97.0
Dont_know 6 2.9 3.0 95.7 100.0
Scantron_Error 0 0.0 0.0 95.7 100.0
NA 9 4.3 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 153 72.9 77.3 72.9 77.3
Somewhat_serious 27 12.9 13.6 85.7 90.9
Very_serious 7 3.3 3.5 89.0 94.4
Dont_know 11 5.2 5.6 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 12 5.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 258 81.9 84.6 81.9 84.6
Somewhat_serious 33 10.5 10.8 92.4 95.4
Very_serious 9 2.9 3.0 95.2 98.4
Dont_know 5 1.6 1.6 96.8 100.0
Scantron_Error 0 0.0 0.0 96.8 100.0
NA 10 3.2 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 229 72.7 77.1 72.7 77.1
Somewhat_serious 47 14.9 15.8 87.6 92.9
Very_serious 8 2.5 2.7 90.2 95.6
Dont_know 13 4.1 4.4 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 18 5.7 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 209 66.3 70.4 66.3 70.4
Somewhat_serious 54 17.1 18.2 83.5 88.6
Very_serious 13 4.1 4.4 87.6 92.9
Dont_know 21 6.7 7.1 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 18 5.7 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 261 73.3 77.0 73.3 77.0
Somewhat_serious 55 15.4 16.2 88.8 93.2
Very_serious 14 3.9 4.1 92.7 97.3
Dont_know 9 2.5 2.7 95.2 100.0
Scantron_Error 0 0.0 0.0 95.2 100.0
NA 17 4.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 233 65.4 71.5 65.4 71.5
Somewhat_serious 63 17.7 19.3 83.1 90.8
Very_serious 15 4.2 4.6 87.4 95.4
Dont_know 15 4.2 4.6 91.6 100.0
Scantron_Error 0 0.0 0.0 91.6 100.0
NA 30 8.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 230 64.6 71.2 64.6 71.2
Somewhat_serious 59 16.6 18.3 81.2 89.5
Very_serious 9 2.5 2.8 83.7 92.3
Dont_know 25 7.0 7.7 90.7 100.0
Scantron_Error 0 0.0 0.0 90.7 100.0
NA 33 9.3 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 456 77.9 80.4 77.9 80.4
Somewhat_serious 69 11.8 12.2 89.7 92.6
Very_serious 21 3.6 3.7 93.3 96.3
Dont_know 20 3.4 3.5 96.8 99.8
Scantron_Error 1 0.2 0.2 96.9 100.0
NA 18 3.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 398 68.0 75.5 68.0 75.5
Somewhat_serious 89 15.2 16.9 83.2 92.4
Very_serious 13 2.2 2.5 85.5 94.9
Dont_know 25 4.3 4.7 89.7 99.6
Scantron_Error 2 0.3 0.4 90.1 100.0
NA 58 9.9 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 407 69.6 76.9 69.6 76.9
Somewhat_serious 64 10.9 12.1 80.5 89.0
Very_serious 19 3.2 3.6 83.8 92.6
Dont_know 38 6.5 7.2 90.3 99.8
Scantron_Error 1 0.2 0.2 90.4 100.0
NA 56 9.6 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1443 82.3 85.5 82.3 85.5
Somewhat_serious 176 10.0 10.4 92.3 95.9
Very_serious 29 1.7 1.7 94.0 97.6
Dont_know 39 2.2 2.3 96.2 99.9
Scantron_Error 1 0.1 0.1 96.2 100.0
NA 66 3.8 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1311 74.7 80.7 74.7 80.7
Somewhat_serious 225 12.8 13.9 87.6 94.6
Very_serious 32 1.8 2.0 89.4 96.6
Dont_know 55 3.1 3.4 92.5 99.9
Scantron_Error 1 0.1 0.1 92.6 100.0
NA 130 7.4 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1285 73.3 79.7 73.3 79.7
Somewhat_serious 185 10.5 11.5 83.8 91.1
Very_serious 47 2.7 2.9 86.5 94.0
Dont_know 95 5.4 5.9 91.9 99.9
Scantron_Error 1 0.1 0.1 92.0 100.0
NA 141 8.0 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 11 68.8 68.8 68.8 68.8
Somewhat_serious 4 25.0 25.0 93.8 93.8
Very_serious 1 6.2 6.2 100.0 100.0
Dont_know 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 12 75.0 80.0 75.0 80.0
Somewhat_serious 2 12.5 13.3 87.5 93.3
Very_serious 1 6.2 6.7 93.8 100.0
Dont_know 0 0.0 0.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 12 75.0 80 75.0 80
Somewhat_serious 3 18.8 20 93.8 100
Very_serious 0 0.0 0 93.8 100
Dont_know 0 0.0 0 93.8 100
Scantron_Error 0 0.0 0 93.8 100
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100 100.0 100

C3C

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know

All data

  c3c1 <- as.factor(d[,"c3c1"])
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 2832 79.6 82.6 79.6 82.6
Somewhat_serious 371 10.4 10.8 90.0 93.4
Very_serious 152 4.3 4.4 94.3 97.8
Dont_know 73 2.1 2.1 96.4 99.9
Scantron_Error 2 0.1 0.1 96.4 100.0
NA 127 3.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 2637 74.1 79.9 74.1 79.9
Somewhat_serious 453 12.7 13.7 86.9 93.6
Very_serious 103 2.9 3.1 89.8 96.7
Dont_know 107 3.0 3.2 92.8 99.9
Scantron_Error 2 0.1 0.1 92.8 100.0
NA 255 7.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 2561 72.0 77.9 72.0 77.9
Somewhat_serious 430 12.1 13.1 84.1 91.0
Very_serious 121 3.4 3.7 87.5 94.7
Dont_know 172 4.8 5.2 92.3 99.9
Scantron_Error 2 0.1 0.1 92.4 100.0
NA 271 7.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 235 73.2 75.3 73.2 75.3
Somewhat_serious 53 16.5 17.0 89.7 92.3
Very_serious 21 6.5 6.7 96.3 99.0
Dont_know 3 0.9 1.0 97.2 100.0
Scantron_Error 0 0.0 0.0 97.2 100.0
NA 9 2.8 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 234 72.9 76.2 72.9 76.2
Somewhat_serious 57 17.8 18.6 90.7 94.8
Very_serious 11 3.4 3.6 94.1 98.4
Dont_know 5 1.6 1.6 95.6 100.0
Scantron_Error 0 0.0 0.0 95.6 100.0
NA 14 4.4 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 253 78.8 81.9 78.8 81.9
Somewhat_serious 38 11.8 12.3 90.7 94.2
Very_serious 9 2.8 2.9 93.5 97.1
Dont_know 9 2.8 2.9 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 12 3.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 157 74.8 77.0 74.8 77.0
Somewhat_serious 27 12.9 13.2 87.6 90.2
Very_serious 17 8.1 8.3 95.7 98.5
Dont_know 3 1.4 1.5 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 6 2.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 156 74.3 77.6 74.3 77.6
Somewhat_serious 29 13.8 14.4 88.1 92.0
Very_serious 10 4.8 5.0 92.9 97.0
Dont_know 6 2.9 3.0 95.7 100.0
Scantron_Error 0 0.0 0.0 95.7 100.0
NA 9 4.3 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 148 70.5 75.1 70.5 75.1
Somewhat_serious 30 14.3 15.2 84.8 90.4
Very_serious 9 4.3 4.6 89.0 94.9
Dont_know 10 4.8 5.1 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 13 6.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 260 82.5 85.8 82.5 85.8
Somewhat_serious 25 7.9 8.3 90.5 94.1
Very_serious 15 4.8 5.0 95.2 99.0
Dont_know 3 1.0 1.0 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 234 74.3 79.1 74.3 79.1
Somewhat_serious 40 12.7 13.5 87.0 92.6
Very_serious 12 3.8 4.1 90.8 96.6
Dont_know 10 3.2 3.4 94.0 100.0
Scantron_Error 0 0.0 0.0 94.0 100.0
NA 19 6.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 210 66.7 70.9 66.7 70.9
Somewhat_serious 46 14.6 15.5 81.3 86.5
Very_serious 22 7.0 7.4 88.3 93.9
Dont_know 18 5.7 6.1 94.0 100.0
Scantron_Error 0 0.0 0.0 94.0 100.0
NA 19 6.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 260 73.0 75.8 73.0 75.8
Somewhat_serious 49 13.8 14.3 86.8 90.1
Very_serious 23 6.5 6.7 93.3 96.8
Dont_know 11 3.1 3.2 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 13 3.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 226 63.5 68.9 63.5 68.9
Somewhat_serious 69 19.4 21.0 82.9 89.9
Very_serious 23 6.5 7.0 89.3 97.0
Dont_know 10 2.8 3.0 92.1 100.0
Scantron_Error 0 0.0 0.0 92.1 100.0
NA 28 7.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 226 63.5 69.3 63.5 69.3
Somewhat_serious 68 19.1 20.9 82.6 90.2
Very_serious 14 3.9 4.3 86.5 94.5
Dont_know 18 5.1 5.5 91.6 100.0
Scantron_Error 0 0.0 0.0 91.6 100.0
NA 30 8.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 447 76.4 79.7 76.4 79.7
Somewhat_serious 66 11.3 11.8 87.7 91.4
Very_serious 28 4.8 5.0 92.5 96.4
Dont_know 19 3.2 3.4 95.7 99.8
Scantron_Error 1 0.2 0.2 95.9 100.0
NA 24 4.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 407 69.6 76.6 69.6 76.6
Somewhat_serious 85 14.5 16.0 84.1 92.7
Very_serious 15 2.6 2.8 86.7 95.5
Dont_know 23 3.9 4.3 90.6 99.8
Scantron_Error 1 0.2 0.2 90.8 100.0
NA 54 9.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 404 69.1 76.7 69.1 76.7
Somewhat_serious 72 12.3 13.7 81.4 90.3
Very_serious 18 3.1 3.4 84.4 93.7
Dont_know 32 5.5 6.1 89.9 99.8
Scantron_Error 1 0.2 0.2 90.1 100.0
NA 58 9.9 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1460 83.2 86.3 83.2 86.3
Somewhat_serious 149 8.5 8.8 91.7 95.2
Very_serious 48 2.7 2.8 94.5 98.0
Dont_know 33 1.9 2.0 96.4 99.9
Scantron_Error 1 0.1 0.1 96.4 100.0
NA 63 3.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1368 78.0 84.2 78.0 84.2
Somewhat_serious 171 9.7 10.5 87.7 94.8
Very_serious 32 1.8 2.0 89.6 96.7
Dont_know 52 3.0 3.2 92.5 99.9
Scantron_Error 1 0.1 0.1 92.6 100.0
NA 130 7.4 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1308 74.6 80.9 74.6 80.9
Somewhat_serious 174 9.9 10.8 84.5 91.7
Very_serious 49 2.8 3.0 87.3 94.7
Dont_know 84 4.8 5.2 92.1 99.9
Scantron_Error 1 0.1 0.1 92.1 100.0
NA 138 7.9 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 13 81.2 81.2 81.2 81.2
Somewhat_serious 2 12.5 12.5 93.8 93.8
Very_serious 0 0.0 0.0 93.8 93.8
Dont_know 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 12 75.0 80.0 75.0 80.0
Somewhat_serious 2 12.5 13.3 87.5 93.3
Very_serious 0 0.0 0.0 87.5 93.3
Dont_know 1 6.2 6.7 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 12 75.0 80.0 75.0 80.0
Somewhat_serious 2 12.5 13.3 87.5 93.3
Very_serious 0 0.0 0.0 87.5 93.3
Dont_know 1 6.2 6.7 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C3D

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know

All data

  c3d1 <- as.factor(d[,"c3d1"])
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 3139 88.2 91.8 88.2 91.8
Somewhat_serious 114 3.2 3.3 91.5 95.1
Very_serious 33 0.9 1.0 92.4 96.1
Dont_know 131 3.7 3.8 96.1 99.9
Scantron_Error 2 0.1 0.1 96.1 100.0
NA 138 3.9 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 2927 82.3 89.0 82.3 89.0
Somewhat_serious 179 5.0 5.4 87.3 94.4
Very_serious 29 0.8 0.9 88.1 95.3
Dont_know 153 4.3 4.7 92.4 99.9
Scantron_Error 2 0.1 0.1 92.5 100.0
NA 267 7.5 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 2857 80.3 87.0 80.3 87.0
Somewhat_serious 153 4.3 4.7 84.6 91.7
Very_serious 35 1.0 1.1 85.6 92.8
Dont_know 236 6.6 7.2 92.2 99.9
Scantron_Error 2 0.1 0.1 92.3 100.0
NA 274 7.7 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 289 90.0 92.9 90.0 92.9
Somewhat_serious 13 4.0 4.2 94.1 97.1
Very_serious 3 0.9 1.0 95.0 98.1
Dont_know 6 1.9 1.9 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 10 3.1 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 283 88.2 91.0 88.2 91.0
Somewhat_serious 21 6.5 6.8 94.7 97.7
Very_serious 0 0.0 0.0 94.7 97.7
Dont_know 7 2.2 2.3 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 10 3.1 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 278 86.6 90.0 86.6 90.0
Somewhat_serious 14 4.4 4.5 91.0 94.5
Very_serious 1 0.3 0.3 91.3 94.8
Dont_know 16 5.0 5.2 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 12 3.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 188 89.5 92.2 89.5 92.2
Somewhat_serious 10 4.8 4.9 94.3 97.1
Very_serious 0 0.0 0.0 94.3 97.1
Dont_know 6 2.9 2.9 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 6 2.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 176 83.8 88.0 83.8 88.0
Somewhat_serious 15 7.1 7.5 91.0 95.5
Very_serious 1 0.5 0.5 91.4 96.0
Dont_know 8 3.8 4.0 95.2 100.0
Scantron_Error 0 0.0 0.0 95.2 100.0
NA 10 4.8 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 177 84.3 89.4 84.3 89.4
Somewhat_serious 6 2.9 3.0 87.1 92.4
Very_serious 3 1.4 1.5 88.6 93.9
Dont_know 12 5.7 6.1 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 12 5.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 277 87.9 91.4 87.9 91.4
Somewhat_serious 8 2.5 2.6 90.5 94.1
Very_serious 5 1.6 1.7 92.1 95.7
Dont_know 13 4.1 4.3 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 261 82.9 88.5 82.9 88.5
Somewhat_serious 16 5.1 5.4 87.9 93.9
Very_serious 3 1.0 1.0 88.9 94.9
Dont_know 15 4.8 5.1 93.7 100.0
Scantron_Error 0 0.0 0.0 93.7 100.0
NA 20 6.3 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 249 79.0 84.4 79.0 84.4
Somewhat_serious 17 5.4 5.8 84.4 90.2
Very_serious 5 1.6 1.7 86.0 91.9
Dont_know 24 7.6 8.1 93.7 100.0
Scantron_Error 0 0.0 0.0 93.7 100.0
NA 20 6.3 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 306 86.0 89.5 86.0 89.5
Somewhat_serious 15 4.2 4.4 90.2 93.9
Very_serious 5 1.4 1.5 91.6 95.3
Dont_know 16 4.5 4.7 96.1 100.0
Scantron_Error 0 0.0 0.0 96.1 100.0
NA 14 3.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 278 78.1 85.0 78.1 85.0
Somewhat_serious 27 7.6 8.3 85.7 93.3
Very_serious 6 1.7 1.8 87.4 95.1
Dont_know 16 4.5 4.9 91.9 100.0
Scantron_Error 0 0.0 0.0 91.9 100.0
NA 29 8.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 264 74.2 82.2 74.2 82.2
Somewhat_serious 27 7.6 8.4 81.7 90.7
Very_serious 6 1.7 1.9 83.4 92.5
Dont_know 24 6.7 7.5 90.2 100.0
Scantron_Error 0 0.0 0.0 90.2 100.0
NA 35 9.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 502 85.8 89.6 85.8 89.6
Somewhat_serious 19 3.2 3.4 89.1 93.0
Very_serious 10 1.7 1.8 90.8 94.8
Dont_know 28 4.8 5.0 95.6 99.8
Scantron_Error 1 0.2 0.2 95.7 100.0
NA 25 4.3 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 459 78.5 87.6 78.5 87.6
Somewhat_serious 28 4.8 5.3 83.2 92.9
Very_serious 8 1.4 1.5 84.6 94.5
Dont_know 28 4.8 5.3 89.4 99.8
Scantron_Error 1 0.2 0.2 89.6 100.0
NA 61 10.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 458 78.3 87.2 78.3 87.2
Somewhat_serious 22 3.8 4.2 82.1 91.4
Very_serious 6 1.0 1.1 83.1 92.6
Dont_know 38 6.5 7.2 89.6 99.8
Scantron_Error 1 0.2 0.2 89.7 100.0
NA 60 10.3 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1564 89.2 92.9 89.2 92.9
Somewhat_serious 49 2.8 2.9 92.0 95.8
Very_serious 10 0.6 0.6 92.5 96.4
Dont_know 60 3.4 3.6 96.0 99.9
Scantron_Error 1 0.1 0.1 96.0 100.0
NA 70 4.0 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1457 83.1 90.0 83.1 90.0
Somewhat_serious 72 4.1 4.4 87.2 94.4
Very_serious 11 0.6 0.7 87.8 95.1
Dont_know 78 4.4 4.8 92.2 99.9
Scantron_Error 1 0.1 0.1 92.3 100.0
NA 135 7.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1418 80.8 87.5 80.8 87.5
Somewhat_serious 67 3.8 4.1 84.7 91.6
Very_serious 14 0.8 0.9 85.5 92.5
Dont_know 121 6.9 7.5 92.4 99.9
Scantron_Error 1 0.1 0.1 92.4 100.0
NA 133 7.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 13 81.2 86.7 81.2 86.7
Somewhat_serious 0 0.0 0.0 81.2 86.7
Very_serious 0 0.0 0.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 13 81.2 92.9 81.2 92.9
Somewhat_serious 0 0.0 0.0 81.2 92.9
Very_serious 0 0.0 0.0 81.2 92.9
Dont_know 1 6.2 7.1 87.5 100.0
Scantron_Error 0 0.0 0.0 87.5 100.0
NA 2 12.5 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 13 81.2 92.9 81.2 92.9
Somewhat_serious 0 0.0 0.0 81.2 92.9
Very_serious 0 0.0 0.0 81.2 92.9
Dont_know 1 6.2 7.1 87.5 100.0
Scantron_Error 0 0.0 0.0 87.5 100.0
NA 2 12.5 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C4A

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know

All data

  c4a1 <- as.factor(d[,"c4a1"])
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 1283 36.1 37.0 36.1 37.0
Sometimes 1453 40.8 41.9 76.9 78.9
Rarely_Never 672 18.9 19.4 95.8 98.2
Dont_know 61 1.7 1.8 97.5 100.0
Scantron_Error 0 0.0 0.0 97.5 100.0
NA 88 2.5 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 1288 36.2 38.7 36.2 38.7
Sometimes 1437 40.4 43.1 76.6 81.8
Rarely_Never 479 13.5 14.4 90.1 96.2
Dont_know 128 3.6 3.8 93.7 100.0
Scantron_Error 0 0.0 0.0 93.7 100.0
NA 225 6.3 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 1901 53.4 57.6 53.4 57.6
Sometimes 875 24.6 26.5 78.0 84.1
Rarely_Never 313 8.8 9.5 86.8 93.6
Dont_know 211 5.9 6.4 92.8 100.0
Scantron_Error 0 0.0 0.0 92.8 100.0
NA 257 7.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 132 41.1 41.9 41.1 41.9
Sometimes 125 38.9 39.7 80.1 81.6
Rarely_Never 56 17.4 17.8 97.5 99.4
Dont_know 2 0.6 0.6 98.1 100.0
Scantron_Error 0 0.0 0.0 98.1 100.0
NA 6 1.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 125 38.9 40.2 38.9 40.2
Sometimes 137 42.7 44.1 81.6 84.2
Rarely_Never 41 12.8 13.2 94.4 97.4
Dont_know 8 2.5 2.6 96.9 100.0
Scantron_Error 0 0.0 0.0 96.9 100.0
NA 10 3.1 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 200 62.3 64.7 62.3 64.7
Sometimes 75 23.4 24.3 85.7 89.0
Rarely_Never 22 6.9 7.1 92.5 96.1
Dont_know 12 3.7 3.9 96.3 100.0
Scantron_Error 0 0.0 0.0 96.3 100.0
NA 12 3.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 91 43.3 44.6 43.3 44.6
Sometimes 74 35.2 36.3 78.6 80.9
Rarely_Never 37 17.6 18.1 96.2 99.0
Dont_know 2 1.0 1.0 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 6 2.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 91 43.3 45.3 43.3 45.3
Sometimes 81 38.6 40.3 81.9 85.6
Rarely_Never 28 13.3 13.9 95.2 99.5
Dont_know 1 0.5 0.5 95.7 100.0
Scantron_Error 0 0.0 0.0 95.7 100.0
NA 9 4.3 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 124 59.0 62.3 59.0 62.3
Sometimes 54 25.7 27.1 84.8 89.4
Rarely_Never 11 5.2 5.5 90.0 95.0
Dont_know 10 4.8 5.0 94.8 100.0
Scantron_Error 0 0.0 0.0 94.8 100.0
NA 11 5.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 121 38.4 39.8 38.4 39.8
Sometimes 116 36.8 38.2 75.2 78.0
Rarely_Never 63 20.0 20.7 95.2 98.7
Dont_know 4 1.3 1.3 96.5 100.0
Scantron_Error 0 0.0 0.0 96.5 100.0
NA 11 3.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 106 33.7 35.8 33.7 35.8
Sometimes 132 41.9 44.6 75.6 80.4
Rarely_Never 43 13.7 14.5 89.2 94.9
Dont_know 15 4.8 5.1 94.0 100.0
Scantron_Error 0 0.0 0.0 94.0 100.0
NA 19 6.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 168 53.3 57.3 53.3 57.3
Sometimes 72 22.9 24.6 76.2 81.9
Rarely_Never 31 9.8 10.6 86.0 92.5
Dont_know 22 7.0 7.5 93.0 100.0
Scantron_Error 0 0.0 0.0 93.0 100.0
NA 22 7.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 153 43.0 44.7 43.0 44.7
Sometimes 144 40.4 42.1 83.4 86.8
Rarely_Never 38 10.7 11.1 94.1 98.0
Dont_know 7 2.0 2.0 96.1 100.0
Scantron_Error 0 0.0 0.0 96.1 100.0
NA 14 3.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 143 40.2 43.1 40.2 43.1
Sometimes 144 40.4 43.4 80.6 86.4
Rarely_Never 32 9.0 9.6 89.6 96.1
Dont_know 13 3.7 3.9 93.3 100.0
Scantron_Error 0 0.0 0.0 93.3 100.0
NA 24 6.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 196 55.1 60.9 55.1 60.9
Sometimes 87 24.4 27.0 79.5 87.9
Rarely_Never 15 4.2 4.7 83.7 92.5
Dont_know 24 6.7 7.5 90.4 100.0
Scantron_Error 0 0.0 0.0 90.4 100.0
NA 34 9.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 232 39.7 40.5 39.7 40.5
Sometimes 223 38.1 38.9 77.8 79.4
Rarely_Never 102 17.4 17.8 95.2 97.2
Dont_know 16 2.7 2.8 97.9 100.0
Scantron_Error 0 0.0 0.0 97.9 100.0
NA 12 2.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 240 41.0 45.1 41.0 45.1
Sometimes 189 32.3 35.5 73.3 80.6
Rarely_Never 80 13.7 15.0 87.0 95.7
Dont_know 23 3.9 4.3 90.9 100.0
Scantron_Error 0 0.0 0.0 90.9 100.0
NA 53 9.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 310 53.0 58.3 53.0 58.3
Sometimes 131 22.4 24.6 75.4 82.9
Rarely_Never 54 9.2 10.2 84.6 93.0
Dont_know 37 6.3 7.0 90.9 100.0
Scantron_Error 0 0.0 0.0 90.9 100.0
NA 53 9.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 548 31.2 32.0 31.2 32.0
Sometimes 763 43.5 44.5 74.7 76.4
Rarely_Never 374 21.3 21.8 96.1 98.3
Dont_know 30 1.7 1.7 97.8 100.0
Scantron_Error 0 0.0 0.0 97.8 100.0
NA 39 2.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 580 33.1 35.3 33.1 35.3
Sometimes 744 42.4 45.2 75.5 80.5
Rarely_Never 253 14.4 15.4 89.9 95.9
Dont_know 68 3.9 4.1 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 109 6.2 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 895 51.0 54.9 51.0 54.9
Sometimes 451 25.7 27.7 76.7 82.6
Rarely_Never 179 10.2 11.0 86.9 93.6
Dont_know 105 6.0 6.4 92.9 100.0
Scantron_Error 0 0.0 0.0 92.9 100.0
NA 124 7.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 6 37.5 37.5 37.5 37.5
Sometimes 8 50.0 50.0 87.5 87.5
Rarely_Never 2 12.5 12.5 100.0 100.0
Dont_know 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 3 18.8 20.0 18.8 20.0
Sometimes 10 62.5 66.7 81.2 86.7
Rarely_Never 2 12.5 13.3 93.8 100.0
Dont_know 0 0.0 0.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 8 50.0 53.3 50.0 53.3
Sometimes 5 31.2 33.3 81.2 86.7
Rarely_Never 1 6.2 6.7 87.5 93.3
Dont_know 1 6.2 6.7 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C4B

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know

All data

  c4b1 <- as.factor(d[,"c4b1"])
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 1604 45.1 47.1 45.1 47.1
Sometimes 1062 29.9 31.2 75.0 78.3
Rarely_Never 498 14.0 14.6 89.0 92.9
Dont_know 241 6.8 7.1 95.7 100.0
Scantron_Error 1 0.0 0.0 95.8 100.0
NA 151 4.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 1381 38.8 42.2 38.8 42.2
Sometimes 1189 33.4 36.3 72.3 78.6
Rarely_Never 467 13.1 14.3 85.4 92.8
Dont_know 231 6.5 7.1 91.9 99.9
Scantron_Error 3 0.1 0.1 92.0 100.0
NA 286 8.0 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 1891 53.2 58.3 53.2 58.3
Sometimes 756 21.3 23.3 74.4 81.6
Rarely_Never 302 8.5 9.3 82.9 91.0
Dont_know 291 8.2 9.0 91.1 99.9
Scantron_Error 2 0.1 0.1 91.1 100.0
NA 315 8.9 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 119 37.1 37.9 37.1 37.9
Sometimes 118 36.8 37.6 73.8 75.5
Rarely_Never 50 15.6 15.9 89.4 91.4
Dont_know 27 8.4 8.6 97.8 100.0
Scantron_Error 0 0.0 0.0 97.8 100.0
NA 7 2.2 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 106 33.0 34.4 33.0 34.4
Sometimes 140 43.6 45.5 76.6 79.9
Rarely_Never 43 13.4 14.0 90.0 93.8
Dont_know 19 5.9 6.2 96.0 100.0
Scantron_Error 0 0.0 0.0 96.0 100.0
NA 13 4.0 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 189 58.9 61.8 58.9 61.8
Sometimes 71 22.1 23.2 81.0 85.0
Rarely_Never 22 6.9 7.2 87.9 92.2
Dont_know 23 7.2 7.5 95.0 99.7
Scantron_Error 1 0.3 0.3 95.3 100.0
NA 15 4.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 93 44.3 46.5 44.3 46.5
Sometimes 57 27.1 28.5 71.4 75.0
Rarely_Never 38 18.1 19.0 89.5 94.0
Dont_know 12 5.7 6.0 95.2 100.0
Scantron_Error 0 0.0 0.0 95.2 100.0
NA 10 4.8 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 83 39.5 42.3 39.5 42.3
Sometimes 64 30.5 32.7 70.0 75.0
Rarely_Never 38 18.1 19.4 88.1 94.4
Dont_know 11 5.2 5.6 93.3 100.0
Scantron_Error 0 0.0 0.0 93.3 100.0
NA 14 6.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 112 53.3 57.4 53.3 57.4
Sometimes 42 20.0 21.5 73.3 79.0
Rarely_Never 25 11.9 12.8 85.2 91.8
Dont_know 16 7.6 8.2 92.9 100.0
Scantron_Error 0 0.0 0.0 92.9 100.0
NA 15 7.1 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 129 41.0 43.1 41.0 43.1
Sometimes 94 29.8 31.4 70.8 74.6
Rarely_Never 50 15.9 16.7 86.7 91.3
Dont_know 26 8.3 8.7 94.9 100.0
Scantron_Error 0 0.0 0.0 94.9 100.0
NA 16 5.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 95 30.2 32.8 30.2 32.8
Sometimes 104 33.0 35.9 63.2 68.6
Rarely_Never 58 18.4 20.0 81.6 88.6
Dont_know 33 10.5 11.4 92.1 100.0
Scantron_Error 0 0.0 0.0 92.1 100.0
NA 25 7.9 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 127 40.3 44.3 40.3 44.3
Sometimes 83 26.3 28.9 66.7 73.2
Rarely_Never 35 11.1 12.2 77.8 85.4
Dont_know 42 13.3 14.6 91.1 100.0
Scantron_Error 0 0.0 0.0 91.1 100.0
NA 28 8.9 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 162 45.5 47.9 45.5 47.9
Sometimes 114 32.0 33.7 77.5 81.7
Rarely_Never 39 11.0 11.5 88.5 93.2
Dont_know 23 6.5 6.8 94.9 100.0
Scantron_Error 0 0.0 0.0 94.9 100.0
NA 18 5.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 143 40.2 43.9 40.2 43.9
Sometimes 127 35.7 39.0 75.8 82.8
Rarely_Never 38 10.7 11.7 86.5 94.5
Dont_know 18 5.1 5.5 91.6 100.0
Scantron_Error 0 0.0 0.0 91.6 100.0
NA 30 8.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 193 54.2 60.7 54.2 60.7
Sometimes 78 21.9 24.5 76.1 85.2
Rarely_Never 22 6.2 6.9 82.3 92.1
Dont_know 25 7.0 7.9 89.3 100.0
Scantron_Error 0 0.0 0.0 89.3 100.0
NA 38 10.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 287 49.1 51.4 49.1 51.4
Sometimes 167 28.5 29.9 77.6 81.4
Rarely_Never 77 13.2 13.8 90.8 95.2
Dont_know 26 4.4 4.7 95.2 99.8
Scantron_Error 1 0.2 0.2 95.4 100.0
NA 27 4.6 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 251 42.9 48.3 42.9 48.3
Sometimes 167 28.5 32.1 71.5 80.4
Rarely_Never 70 12.0 13.5 83.4 93.8
Dont_know 31 5.3 6.0 88.7 99.8
Scantron_Error 1 0.2 0.2 88.9 100.0
NA 65 11.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 313 53.5 60.3 53.5 60.3
Sometimes 115 19.7 22.2 73.2 82.5
Rarely_Never 52 8.9 10.0 82.1 92.5
Dont_know 38 6.5 7.3 88.5 99.8
Scantron_Error 1 0.2 0.2 88.7 100.0
NA 66 11.3 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 809 46.1 48.1 46.1 48.1
Sometimes 504 28.7 30.0 74.9 78.1
Rarely_Never 243 13.9 14.4 88.7 92.5
Dont_know 126 7.2 7.5 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 72 4.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 700 39.9 43.3 39.9 43.3
Sometimes 580 33.1 35.9 73.0 79.2
Rarely_Never 218 12.4 13.5 85.4 92.7
Dont_know 116 6.6 7.2 92.0 99.9
Scantron_Error 2 0.1 0.1 92.1 100.0
NA 138 7.9 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 950 54.2 59.3 54.2 59.3
Sometimes 362 20.6 22.6 74.8 81.9
Rarely_Never 146 8.3 9.1 83.1 91.0
Dont_know 144 8.2 9.0 91.3 100.0
Scantron_Error 0 0.0 0.0 91.3 100.0
NA 152 8.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 5 31.2 33.3 31.2 33.3
Sometimes 8 50.0 53.3 81.2 86.7
Rarely_Never 1 6.2 6.7 87.5 93.3
Dont_know 1 6.2 6.7 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 3 18.8 20.0 18.8 20.0
Sometimes 7 43.8 46.7 62.5 66.7
Rarely_Never 2 12.5 13.3 75.0 80.0
Dont_know 3 18.8 20.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 7 43.8 46.7 43.8 46.7
Sometimes 5 31.2 33.3 75.0 80.0
Rarely_Never 0 0.0 0.0 75.0 80.0
Dont_know 3 18.8 20.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C4C

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know

All data

  c4c1 <- as.factor(d[,"c4c1"])
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 998 28.1 29.5 28.1 29.5
Sometimes 1378 38.7 40.7 66.8 70.2
Rarely_Never 944 26.5 27.9 93.3 98.1
Dont_know 62 1.7 1.8 95.1 99.9
Scantron_Error 2 0.1 0.1 95.1 100.0
NA 173 4.9 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 1053 29.6 32.6 29.6 32.6
Sometimes 1383 38.9 42.8 68.5 75.4
Rarely_Never 699 19.7 21.6 88.1 97.0
Dont_know 94 2.6 2.9 90.8 99.9
Scantron_Error 2 0.1 0.1 90.8 100.0
NA 326 9.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 1933 54.3 60.3 54.3 60.3
Sometimes 766 21.5 23.9 75.9 84.1
Rarely_Never 361 10.1 11.3 86.0 95.4
Dont_know 146 4.1 4.6 90.1 99.9
Scantron_Error 2 0.1 0.1 90.2 100.0
NA 349 9.8 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 74 23.1 24.0 23.1 24.0
Sometimes 133 41.4 43.2 64.5 67.2
Rarely_Never 97 30.2 31.5 94.7 98.7
Dont_know 4 1.2 1.3 96.0 100.0
Scantron_Error 0 0.0 0.0 96.0 100.0
NA 13 4.0 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 85 26.5 28.1 26.5 28.1
Sometimes 141 43.9 46.5 70.4 74.6
Rarely_Never 75 23.4 24.8 93.8 99.3
Dont_know 2 0.6 0.7 94.4 100.0
Scantron_Error 0 0.0 0.0 94.4 100.0
NA 18 5.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 194 60.4 64.7 60.4 64.7
Sometimes 62 19.3 20.7 79.8 85.3
Rarely_Never 35 10.9 11.7 90.7 97.0
Dont_know 9 2.8 3.0 93.5 100.0
Scantron_Error 0 0.0 0.0 93.5 100.0
NA 21 6.5 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 60 28.6 29.6 28.6 29.6
Sometimes 86 41.0 42.4 69.5 71.9
Rarely_Never 51 24.3 25.1 93.8 97.0
Dont_know 6 2.9 3.0 96.7 100.0
Scantron_Error 0 0.0 0.0 96.7 100.0
NA 7 3.3 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 58 27.6 29.3 27.6 29.3
Sometimes 95 45.2 48.0 72.9 77.3
Rarely_Never 41 19.5 20.7 92.4 98.0
Dont_know 4 1.9 2.0 94.3 100.0
Scantron_Error 0 0.0 0.0 94.3 100.0
NA 12 5.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 121 57.6 61.7 57.6 61.7
Sometimes 47 22.4 24.0 80.0 85.7
Rarely_Never 17 8.1 8.7 88.1 94.4
Dont_know 11 5.2 5.6 93.3 100.0
Scantron_Error 0 0.0 0.0 93.3 100.0
NA 14 6.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 80 25.4 27.1 25.4 27.1
Sometimes 114 36.2 38.6 61.6 65.8
Rarely_Never 100 31.7 33.9 93.3 99.7
Dont_know 1 0.3 0.3 93.7 100.0
Scantron_Error 0 0.0 0.0 93.7 100.0
NA 20 6.3 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 72 22.9 25.4 22.9 25.4
Sometimes 116 36.8 41.0 59.7 66.4
Rarely_Never 82 26.0 29.0 85.7 95.4
Dont_know 13 4.1 4.6 89.8 100.0
Scantron_Error 0 0.0 0.0 89.8 100.0
NA 32 10.2 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 160 50.8 56.3 50.8 56.3
Sometimes 70 22.2 24.6 73.0 81.0
Rarely_Never 37 11.7 13.0 84.8 94.0
Dont_know 17 5.4 6.0 90.2 100.0
Scantron_Error 0 0.0 0.0 90.2 100.0
NA 31 9.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 94 26.4 28.1 26.4 28.1
Sometimes 143 40.2 42.7 66.6 70.7
Rarely_Never 93 26.1 27.8 92.7 98.5
Dont_know 5 1.4 1.5 94.1 100.0
Scantron_Error 0 0.0 0.0 94.1 100.0
NA 21 5.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 114 32.0 35.5 32.0 35.5
Sometimes 132 37.1 41.1 69.1 76.6
Rarely_Never 66 18.5 20.6 87.6 97.2
Dont_know 9 2.5 2.8 90.2 100.0
Scantron_Error 0 0.0 0.0 90.2 100.0
NA 35 9.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 179 50.3 57.6 50.3 57.6
Sometimes 76 21.3 24.4 71.6 82.0
Rarely_Never 40 11.2 12.9 82.9 94.9
Dont_know 15 4.2 4.8 87.1 99.7
Scantron_Error 1 0.3 0.3 87.4 100.0
NA 45 12.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 222 37.9 39.7 37.9 39.7
Sometimes 209 35.7 37.4 73.7 77.1
Rarely_Never 112 19.1 20.0 92.8 97.1
Dont_know 15 2.6 2.7 95.4 99.8
Scantron_Error 1 0.2 0.2 95.6 100.0
NA 26 4.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 213 36.4 41.1 36.4 41.1
Sometimes 222 37.9 42.9 74.4 84.0
Rarely_Never 68 11.6 13.1 86.0 97.1
Dont_know 14 2.4 2.7 88.4 99.8
Scantron_Error 1 0.2 0.2 88.5 100.0
NA 67 11.5 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 314 53.7 60.5 53.7 60.5
Sometimes 126 21.5 24.3 75.2 84.8
Rarely_Never 57 9.7 11.0 85.0 95.8
Dont_know 21 3.6 4.0 88.5 99.8
Scantron_Error 1 0.2 0.2 88.7 100.0
NA 66 11.3 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 463 26.4 27.7 26.4 27.7
Sometimes 688 39.2 41.2 65.6 69.0
Rarely_Never 488 27.8 29.2 93.4 98.2
Dont_know 29 1.7 1.7 95.1 99.9
Scantron_Error 1 0.1 0.1 95.2 100.0
NA 85 4.8 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 508 29.0 31.8 29.0 31.8
Sometimes 671 38.3 42.1 67.2 73.9
Rarely_Never 364 20.8 22.8 88.0 96.7
Dont_know 51 2.9 3.2 90.9 99.9
Scantron_Error 1 0.1 0.1 90.9 100.0
NA 159 9.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 959 54.7 60.5 54.7 60.5
Sometimes 379 21.6 23.9 76.3 84.4
Rarely_Never 175 10.0 11.0 86.3 95.5
Dont_know 72 4.1 4.5 90.4 100.0
Scantron_Error 0 0.0 0.0 90.4 100.0
NA 169 9.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 5 31.2 33.3 31.2 33.3
Sometimes 5 31.2 33.3 62.5 66.7
Rarely_Never 3 18.8 20.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 3 18.8 23.1 18.8 23.1
Sometimes 6 37.5 46.2 56.2 69.2
Rarely_Never 3 18.8 23.1 75.0 92.3
Dont_know 1 6.2 7.7 81.2 100.0
Scantron_Error 0 0.0 0.0 81.2 100.0
NA 3 18.8 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 6 37.5 46.2 37.5 46.2
Sometimes 6 37.5 46.2 75.0 92.3
Rarely_Never 0 0.0 0.0 75.0 92.3
Dont_know 1 6.2 7.7 81.2 100.0
Scantron_Error 0 0.0 0.0 81.2 100.0
NA 3 18.8 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C4D

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know

All data

  c4d1 <- as.factor(d[,"c4d1"])
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 463 13.0 13.6 13.0 13.6
Sometimes 1249 35.1 36.8 48.1 50.5
Rarely_Never 1617 45.5 47.7 93.6 98.1
Dont_know 61 1.7 1.8 95.3 99.9
Scantron_Error 3 0.1 0.1 95.4 100.0
NA 164 4.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 588 16.5 18.1 16.5 18.1
Sometimes 1418 39.9 43.6 56.4 61.7
Rarely_Never 1132 31.8 34.8 88.2 96.5
Dont_know 112 3.1 3.4 91.4 100.0
Scantron_Error 1 0.0 0.0 91.4 100.0
NA 306 8.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 1418 39.9 43.9 39.9 43.9
Sometimes 987 27.7 30.5 67.6 74.4
Rarely_Never 631 17.7 19.5 85.4 94.0
Dont_know 194 5.5 6.0 90.8 100.0
Scantron_Error 1 0.0 0.0 90.8 100.0
NA 326 9.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 37 11.5 11.9 11.5 11.9
Sometimes 117 36.4 37.5 48.0 49.4
Rarely_Never 153 47.7 49.0 95.6 98.4
Dont_know 5 1.6 1.6 97.2 100.0
Scantron_Error 0 0.0 0.0 97.2 100.0
NA 9 2.8 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 53 16.5 17.2 16.5 17.2
Sometimes 133 41.4 43.2 57.9 60.4
Rarely_Never 116 36.1 37.7 94.1 98.1
Dont_know 6 1.9 1.9 96.0 100.0
Scantron_Error 0 0.0 0.0 96.0 100.0
NA 13 4.0 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 163 50.8 53.3 50.8 53.3
Sometimes 83 25.9 27.1 76.6 80.4
Rarely_Never 43 13.4 14.1 90.0 94.4
Dont_know 17 5.3 5.6 95.3 100.0
Scantron_Error 0 0.0 0.0 95.3 100.0
NA 15 4.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 35 16.7 17.5 16.7 17.5
Sometimes 67 31.9 33.5 48.6 51.0
Rarely_Never 95 45.2 47.5 93.8 98.5
Dont_know 3 1.4 1.5 95.2 100.0
Scantron_Error 0 0.0 0.0 95.2 100.0
NA 10 4.8 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 43 20.5 21.9 20.5 21.9
Sometimes 79 37.6 40.3 58.1 62.2
Rarely_Never 71 33.8 36.2 91.9 98.5
Dont_know 3 1.4 1.5 93.3 100.0
Scantron_Error 0 0.0 0.0 93.3 100.0
NA 14 6.7 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 87 41.4 44.8 41.4 44.8
Sometimes 62 29.5 32.0 71.0 76.8
Rarely_Never 35 16.7 18.0 87.6 94.8
Dont_know 10 4.8 5.2 92.4 100.0
Scantron_Error 0 0.0 0.0 92.4 100.0
NA 16 7.6 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 47 14.9 15.5 14.9 15.5
Sometimes 110 34.9 36.3 49.8 51.8
Rarely_Never 143 45.4 47.2 95.2 99.0
Dont_know 3 1.0 1.0 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 38 12.1 13.1 12.1 13.1
Sometimes 119 37.8 40.9 49.8 54.0
Rarely_Never 119 37.8 40.9 87.6 94.8
Dont_know 15 4.8 5.2 92.4 100.0
Scantron_Error 0 0.0 0.0 92.4 100.0
NA 24 7.6 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 116 36.8 39.6 36.8 39.6
Sometimes 99 31.4 33.8 68.3 73.4
Rarely_Never 58 18.4 19.8 86.7 93.2
Dont_know 20 6.3 6.8 93.0 100.0
Scantron_Error 0 0.0 0.0 93.0 100.0
NA 22 7.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 48 13.5 14.2 13.5 14.2
Sometimes 140 39.3 41.4 52.8 55.6
Rarely_Never 140 39.3 41.4 92.1 97.0
Dont_know 10 2.8 3.0 94.9 100.0
Scantron_Error 0 0.0 0.0 94.9 100.0
NA 18 5.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 62 17.4 19.3 17.4 19.3
Sometimes 162 45.5 50.5 62.9 69.8
Rarely_Never 83 23.3 25.9 86.2 95.6
Dont_know 14 3.9 4.4 90.2 100.0
Scantron_Error 0 0.0 0.0 90.2 100.0
NA 35 9.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 135 37.9 42.9 37.9 42.9
Sometimes 93 26.1 29.5 64.0 72.4
Rarely_Never 63 17.7 20.0 81.7 92.4
Dont_know 24 6.7 7.6 88.5 100.0
Scantron_Error 0 0.0 0.0 88.5 100.0
NA 41 11.5 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 106 18.1 19.2 18.1 19.2
Sometimes 217 37.1 39.3 55.2 58.5
Rarely_Never 216 36.9 39.1 92.1 97.6
Dont_know 11 1.9 2.0 94.0 99.6
Scantron_Error 2 0.3 0.4 94.4 100.0
NA 33 5.6 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 121 20.7 23.4 20.7 23.4
Sometimes 230 39.3 44.4 60.0 67.8
Rarely_Never 149 25.5 28.8 85.5 96.5
Dont_know 17 2.9 3.3 88.4 99.8
Scantron_Error 1 0.2 0.2 88.5 100.0
NA 67 11.5 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 230 39.3 44.7 39.3 44.7
Sometimes 163 27.9 31.7 67.2 76.5
Rarely_Never 97 16.6 18.9 83.8 95.3
Dont_know 23 3.9 4.5 87.7 99.8
Scantron_Error 1 0.2 0.2 87.9 100.0
NA 71 12.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 183 10.4 10.9 10.4 10.9
Sometimes 593 33.8 35.5 44.2 46.4
Rarely_Never 866 49.4 51.8 93.6 98.2
Dont_know 29 1.7 1.7 95.3 99.9
Scantron_Error 1 0.1 0.1 95.3 100.0
NA 82 4.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 266 15.2 16.6 15.2 16.6
Sometimes 689 39.3 43.0 54.4 59.6
Rarely_Never 590 33.6 36.8 88.1 96.4
Dont_know 57 3.2 3.6 91.3 100.0
Scantron_Error 0 0.0 0.0 91.3 100.0
NA 152 8.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 678 38.7 42.5 38.7 42.5
Sometimes 482 27.5 30.2 66.1 72.8
Rarely_Never 334 19.0 21.0 85.2 93.7
Dont_know 100 5.7 6.3 90.9 100.0
Scantron_Error 0 0.0 0.0 90.9 100.0
NA 160 9.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 7 43.8 43.8 43.8 43.8
Sometimes 5 31.2 31.2 75.0 75.0
Rarely_Never 4 25.0 25.0 100.0 100.0
Dont_know 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 5 31.2 33.3 31.2 33.3
Sometimes 6 37.5 40.0 68.8 73.3
Rarely_Never 4 25.0 26.7 93.8 100.0
Dont_know 0 0.0 0.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 9 56.2 60.0 56.2 60.0
Sometimes 5 31.2 33.3 87.5 93.3
Rarely_Never 1 6.2 6.7 93.8 100.0
Dont_know 0 0.0 0.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

C4E

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know

All data

  c4e1 <- as.factor(d[,"c4e1"])
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 562 15.8 17.1 15.8 17.1
Sometimes 1104 31.0 33.5 46.8 50.6
Rarely_Never 1327 37.3 40.3 84.1 90.9
Dont_know 296 8.3 9.0 92.5 99.9
Scantron_Error 4 0.1 0.1 92.6 100.0
NA 264 7.4 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 613 17.2 19.4 17.2 19.4
Sometimes 1186 33.3 37.6 50.6 57.0
Rarely_Never 1087 30.6 34.4 81.1 91.4
Dont_know 270 7.6 8.5 88.7 99.9
Scantron_Error 2 0.1 0.1 88.8 100.0
NA 399 11.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 1253 35.2 39.8 35.2 39.8
Sometimes 947 26.6 30.1 61.8 69.8
Rarely_Never 641 18.0 20.3 79.9 90.2
Dont_know 307 8.6 9.7 88.5 99.9
Scantron_Error 2 0.1 0.1 88.6 100.0
NA 407 11.4 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 39 12.1 12.8 12.1 12.8
Sometimes 105 32.7 34.4 44.9 47.2
Rarely_Never 141 43.9 46.2 88.8 93.4
Dont_know 20 6.2 6.6 95.0 100.0
Scantron_Error 0 0.0 0.0 95.0 100.0
NA 16 5.0 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 43 13.4 14.2 13.4 14.2
Sometimes 135 42.1 44.7 55.5 58.9
Rarely_Never 107 33.3 35.4 88.8 94.4
Dont_know 16 5.0 5.3 93.8 99.7
Scantron_Error 1 0.3 0.3 94.1 100.0
NA 19 5.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 139 43.3 46.2 43.3 46.2
Sometimes 88 27.4 29.2 70.7 75.4
Rarely_Never 51 15.9 16.9 86.6 92.4
Dont_know 23 7.2 7.6 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 20 6.2 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 38 18.1 19.3 18.1 19.3
Sometimes 61 29.0 31.0 47.1 50.3
Rarely_Never 86 41.0 43.7 88.1 93.9
Dont_know 11 5.2 5.6 93.3 99.5
Scantron_Error 1 0.5 0.5 93.8 100.0
NA 13 6.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 43 20.5 22.3 20.5 22.3
Sometimes 78 37.1 40.4 57.6 62.7
Rarely_Never 60 28.6 31.1 86.2 93.8
Dont_know 12 5.7 6.2 91.9 100.0
Scantron_Error 0 0.0 0.0 91.9 100.0
NA 17 8.1 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 75 35.7 39.1 35.7 39.1
Sometimes 59 28.1 30.7 63.8 69.8
Rarely_Never 41 19.5 21.4 83.3 91.1
Dont_know 17 8.1 8.9 91.4 100.0
Scantron_Error 0 0.0 0.0 91.4 100.0
NA 18 8.6 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 51 16.2 17.7 16.2 17.7
Sometimes 109 34.6 37.8 50.8 55.6
Rarely_Never 106 33.7 36.8 84.4 92.4
Dont_know 22 7.0 7.6 91.4 100.0
Scantron_Error 0 0.0 0.0 91.4 100.0
NA 27 8.6 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 43 13.7 15.5 13.7 15.5
Sometimes 102 32.4 36.8 46.0 52.3
Rarely_Never 105 33.3 37.9 79.4 90.3
Dont_know 27 8.6 9.7 87.9 100.0
Scantron_Error 0 0.0 0.0 87.9 100.0
NA 38 12.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 99 31.4 35.4 31.4 35.4
Sometimes 91 28.9 32.5 60.3 67.9
Rarely_Never 63 20.0 22.5 80.3 90.4
Dont_know 27 8.6 9.6 88.9 100.0
Scantron_Error 0 0.0 0.0 88.9 100.0
NA 35 11.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 51 14.3 15.4 14.3 15.4
Sometimes 116 32.6 34.9 46.9 50.3
Rarely_Never 134 37.6 40.4 84.6 90.7
Dont_know 31 8.7 9.3 93.3 100.0
Scantron_Error 0 0.0 0.0 93.3 100.0
NA 24 6.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 55 15.4 17.3 15.4 17.3
Sometimes 123 34.6 38.7 50.0 56.0
Rarely_Never 110 30.9 34.6 80.9 90.6
Dont_know 30 8.4 9.4 89.3 100.0
Scantron_Error 0 0.0 0.0 89.3 100.0
NA 38 10.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 117 32.9 37.4 32.9 37.4
Sometimes 101 28.4 32.3 61.2 69.6
Rarely_Never 63 17.7 20.1 78.9 89.8
Dont_know 32 9.0 10.2 87.9 100.0
Scantron_Error 0 0.0 0.0 87.9 100.0
NA 43 12.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 118 20.2 22.0 20.2 22.0
Sometimes 180 30.8 33.5 50.9 55.5
Rarely_Never 187 32.0 34.8 82.9 90.3
Dont_know 51 8.7 9.5 91.6 99.8
Scantron_Error 1 0.2 0.2 91.8 100.0
NA 48 8.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 120 20.5 23.9 20.5 23.9
Sometimes 185 31.6 36.9 52.1 60.8
Rarely_Never 156 26.7 31.1 78.8 91.8
Dont_know 40 6.8 8.0 85.6 99.8
Scantron_Error 1 0.2 0.2 85.8 100.0
NA 83 14.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 207 35.4 41.2 35.4 41.2
Sometimes 149 25.5 29.7 60.9 70.9
Rarely_Never 94 16.1 18.7 76.9 89.6
Dont_know 50 8.5 10.0 85.5 99.6
Scantron_Error 2 0.3 0.4 85.8 100.0
NA 83 14.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 264 15.1 16.3 15.1 16.3
Sometimes 527 30.0 32.6 45.1 48.9
Rarely_Never 667 38.0 41.2 83.1 90.1
Dont_know 159 9.1 9.8 92.2 99.9
Scantron_Error 2 0.1 0.1 92.3 100.0
NA 135 7.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 308 17.6 19.9 17.6 19.9
Sometimes 557 31.8 35.9 49.3 55.8
Rarely_Never 543 31.0 35.0 80.3 90.8
Dont_know 143 8.2 9.2 88.4 100.0
Scantron_Error 0 0.0 0.0 88.4 100.0
NA 203 11.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 612 34.9 39.6 34.9 39.6
Sometimes 453 25.8 29.3 60.7 68.8
Rarely_Never 326 18.6 21.1 79.3 89.9
Dont_know 156 8.9 10.1 88.2 100.0
Scantron_Error 0 0.0 0.0 88.2 100.0
NA 207 11.8 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 1 6.2 6.7 6.2 6.7
Sometimes 6 37.5 40.0 43.8 46.7
Rarely_Never 6 37.5 40.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 1 6.2 6.7 6.2 6.7
Sometimes 6 37.5 40.0 43.8 46.7
Rarely_Never 6 37.5 40.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 4 25.0 26.7 25.0 26.7
Sometimes 6 37.5 40.0 62.5 66.7
Rarely_Never 3 18.8 20.0 81.2 86.7
Dont_know 2 12.5 13.3 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0

D1

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely ## All data
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
  levels(d1aa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1aa <- ordered(d1aa, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1ab <- ordered(d1ab, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 1779 50.0 52.0
Yes 1639 46.1 47.9
Scantron_Error 1 0.0 0.0
NA 138 3.9 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 11 0.3 100 0.3 100
NA 3546 99.7 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  levels(d1ba) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ba <- ordered(d1ba, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1bb <- ordered(d1bb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 2072 58.3 61.6
Yes 1289 36.2 38.3
Scantron_Error 1 0.0 0.0
NA 195 5.5 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 9 0.3 100 0.3 100
NA 3548 99.7 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  levels(d1ca) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ca <- ordered(d1ca, c( "No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 1682 47.3 49.4
Yes 1721 48.4 50.6
Scantron_Error 0 0.0 0.0
NA 154 4.3 NA
Total 3557 100.0 100.0
  d1cb <- as.factor(d[,"d1cb"])
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1cb <- ordered(d1cb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 11 0.3 100 0.3 100
NA 3546 99.7 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  levels(d1da) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1da <- ordered(d1da, c( "No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 2714 76.3 79.8
Yes 689 19.4 20.2
Scantron_Error 0 0.0 0.0
NA 154 4.3 NA
Total 3557 100.0 100.0
  d1db <- as.factor(d[,"d1db"])
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1db <- ordered(d1db, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 7 0.2 100 0.2 100
NA 3550 99.8 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  levels(d1ea) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ea <- ordered(d1ea, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 2863 80.5 83.6
Yes 561 15.8 16.4
Scantron_Error 0 0.0 0.0
NA 133 3.7 NA
Total 3557 100.0 100.0
  d1eb <- as.factor(d[,"d1eb"])
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1eb <- ordered(d1eb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 6 0.2 100 0.2 100
NA 3551 99.8 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  levels(d1fa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1fa <- ordered(d1fa, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 2439 68.6 71.9
Yes 953 26.8 28.1
Scantron_Error 0 0.0 0.0
NA 165 4.6 NA
Total 3557 100.0 100.0
  d1fb <- as.factor(d[,"d1fb"])
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1fb <- ordered(d1fb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 7 0.2 100 0.2 100
NA 3550 99.8 NA 100.0 NA
Total 3557 100.0 100 100.0 100
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  levels(d1ga) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ga <- ordered(d1ga, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 2856 80.3 84
Yes 543 15.3 16
Scantron_Error 0 0.0 0
NA 158 4.4 NA
Total 3557 100.0 100
  d1gb <- as.factor(d[,"d1gb"])
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1gb <- ordered(d1gb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 4 0.1 100 0.1 100
NA 3553 99.9 NA 100.0 NA
Total 3557 100.0 100 100.0 100

LA County

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 158 49.2 50.3
Yes 156 48.6 49.7
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 177 55.1 56.7
Yes 135 42.1 43.3
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 70 21.8 22.2
Yes 245 76.3 77.8
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 222 69.2 71.8
Yes 87 27.1 28.2
Scantron_Error 0 0.0 0.0
NA 12 3.7 NA
Total 321 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 233 72.6 74.2
Yes 81 25.2 25.8
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 228 71.0 73.3
Yes 83 25.9 26.7
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 256 79.8 81.8
Yes 57 17.8 18.2
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 321 100 NA 100 NA
Total 321 100 100 100 100

Northern CA

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 100 47.6 49.3
Yes 103 49.0 50.7
Scantron_Error 0 0.0 0.0
NA 7 3.3 NA
Total 210 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 120 57.1 58.8
Yes 84 40.0 41.2
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 0.5 100 0.5 100
NA 209 99.5 NA 100.0 NA
Total 210 100.0 100 100.0 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 79 37.6 38.5
Yes 126 60.0 61.5
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 0 0 0
Yes 0 0 0 0 0
Scantron_Error 2 1 100 1 100
NA 208 99 NA 100 NA
Total 210 100 100 100 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 146 69.5 72.3
Yes 56 26.7 27.7
Scantron_Error 0 0.0 0.0
NA 8 3.8 NA
Total 210 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 148 70.5 74.4
Yes 51 24.3 25.6
Scantron_Error 0 0.0 0.0
NA 11 5.2 NA
Total 210 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 150 71.4 75.4
Yes 49 23.3 24.6
Scantron_Error 0 0.0 0.0
NA 11 5.2 NA
Total 210 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 152 72.4 75.6
Yes 49 23.3 24.4
Scantron_Error 0 0.0 0.0
NA 9 4.3 NA
Total 210 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 210 100 NA 100 NA
Total 210 100 100 100 100

Greater CA

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 134 42.5 44.4
Yes 168 53.3 55.6
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 145 46.0 49
Yes 151 47.9 51
Scantron_Error 0 0.0 0
NA 19 6.0 NA
Total 315 100.0 100
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 113 35.9 37.5
Yes 188 59.7 62.5
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 224 71.1 74.4
Yes 77 24.4 25.6
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 2 0.6 100 0.6 100
NA 313 99.4 NA 100.0 NA
Total 315 100.0 100 100.0 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 219 69.5 72.5
Yes 83 26.3 27.5
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 0.3 100 0.3 100
NA 314 99.7 NA 100.0 NA
Total 315 100.0 100 100.0 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 214 67.9 71.3
Yes 86 27.3 28.7
Scantron_Error 0 0.0 0.0
NA 15 4.8 NA
Total 315 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 247 78.4 81.2
Yes 57 18.1 18.8
Scantron_Error 0 0.0 0.0
NA 11 3.5 NA
Total 315 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 315 100 NA 100 NA
Total 315 100 100 100 100

Detroit

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 192 53.9 56.3
Yes 148 41.6 43.4
Scantron_Error 1 0.3 0.3
NA 15 4.2 NA
Total 356 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 0.3 100 0.3 100
NA 355 99.7 NA 100.0 NA
Total 356 100.0 100 100.0 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 212 59.6 64.4
Yes 116 32.6 35.3
Scantron_Error 1 0.3 0.3
NA 27 7.6 NA
Total 356 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 138 38.8 41.2
Yes 197 55.3 58.8
Scantron_Error 0 0.0 0.0
NA 21 5.9 NA
Total 356 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 249 69.9 73.9
Yes 88 24.7 26.1
Scantron_Error 0 0.0 0.0
NA 19 5.3 NA
Total 356 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 275 77.2 80.9
Yes 65 18.3 19.1
Scantron_Error 0 0.0 0.0
NA 16 4.5 NA
Total 356 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 248 69.7 74.3
Yes 86 24.2 25.7
Scantron_Error 0 0.0 0.0
NA 22 6.2 NA
Total 356 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 282 79.2 84.4
Yes 52 14.6 15.6
Scantron_Error 0 0.0 0.0
NA 22 6.2 NA
Total 356 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 356 100 NA 100 NA
Total 356 100 100 100 100

Louisiana

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 317 54.2 57.1
Yes 238 40.7 42.9
Scantron_Error 0 0.0 0.0
NA 30 5.1 NA
Total 585 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 2 0.3 100 0.3 100
NA 583 99.7 NA 100.0 NA
Total 585 100.0 100 100.0 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 364 62.2 66.3
Yes 185 31.6 33.7
Scantron_Error 0 0.0 0.0
NA 36 6.2 NA
Total 585 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 0.2 100 0.2 100
NA 584 99.8 NA 100.0 NA
Total 585 100.0 100 100.0 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 298 50.9 53.6
Yes 258 44.1 46.4
Scantron_Error 0 0.0 0.0
NA 29 5.0 NA
Total 585 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 3 0.5 100 0.5 100
NA 582 99.5 NA 100.0 NA
Total 585 100.0 100 100.0 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 466 79.7 83.2
Yes 94 16.1 16.8
Scantron_Error 0 0.0 0.0
NA 25 4.3 NA
Total 585 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 503 86.0 89.5
Yes 59 10.1 10.5
Scantron_Error 0 0.0 0.0
NA 23 3.9 NA
Total 585 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 383 65.5 68.8
Yes 174 29.7 31.2
Scantron_Error 0 0.0 0.0
NA 28 4.8 NA
Total 585 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 0.2 100 0.2 100
NA 584 99.8 NA 100.0 NA
Total 585 100.0 100 100.0 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 468 80.0 84
Yes 89 15.2 16
Scantron_Error 0 0.0 0
NA 28 4.8 NA
Total 585 100.0 100
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
Scantron_Error 0 0 NaN 0 NaN
NA 585 100 NA 100 NA
Total 585 100 100 100 100

Georgia

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 870 49.6 51.5
Yes 819 46.7 48.5
Scantron_Error 0 0.0 0.0
NA 65 3.7 NA
Total 1754 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 7 0.4 100 0.4 100
NA 1747 99.6 NA 100.0 NA
Total 1754 100.0 100 100.0 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 1047 59.7 63.2
Yes 609 34.7 36.8
Scantron_Error 0 0.0 0.0
NA 98 5.6 NA
Total 1754 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 6 0.3 100 0.3 100
NA 1748 99.7 NA 100.0 NA
Total 1754 100.0 100 100.0 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 976 55.6 58.3
Yes 699 39.9 41.7
Scantron_Error 0 0.0 0.0
NA 79 4.5 NA
Total 1754 100.0 100.0
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 5 0.3 100 0.3 100
NA 1749 99.7 NA 100.0 NA
Total 1754 100.0 100 100.0 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 1399 79.8 83.4
Yes 279 15.9 16.6
Scantron_Error 0 0.0 0.0
NA 76 4.3 NA
Total 1754 100.0 100.0
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 4 0.2 100 0.2 100
NA 1750 99.8 NA 100.0 NA
Total 1754 100.0 100 100.0 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 1471 83.9 87
Yes 220 12.5 13
Scantron_Error 0 0.0 0
NA 63 3.6 NA
Total 1754 100.0 100
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 4 0.2 100 0.2 100
NA 1750 99.8 NA 100.0 NA
Total 1754 100.0 100 100.0 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 1203 68.6 71.8
Yes 472 26.9 28.2
Scantron_Error 0 0.0 0.0
NA 79 4.5 NA
Total 1754 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 5 0.3 100 0.3 100
NA 1749 99.7 NA 100.0 NA
Total 1754 100.0 100 100.0 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 1437 81.9 85.8
Yes 237 13.5 14.2
Scantron_Error 0 0.0 0.0
NA 80 4.6 NA
Total 1754 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 3 0.2 100 0.2 100
NA 1751 99.8 NA 100.0 NA
Total 1754 100.0 100 100.0 100

Michigan

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 8 50.0 53.3
Yes 7 43.8 46.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 7 43.8 43.8
Yes 9 56.2 56.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 8 50 50
Yes 8 50 50
Scantron_Error 0 0 0
Total 16 100 100
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 8 50 50
Yes 8 50 50
Scantron_Error 0 0 0
Total 16 100 100
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 14 87.5 87.5
Yes 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
f. Have you ever been unfairly denied a bank loan?
n % val%
No 13 81.2 81.2
Yes 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 14 87.5 87.5
Yes 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0.0 0 0.0 0
Yes 0 0.0 0 0.0 0
Scantron_Error 1 6.2 100 6.2 100
NA 15 93.8 NA 100.0 NA
Total 16 100.0 100 100.0 100

D2

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.

All data

# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 495 13.9 14.6
Somewhat_Agree 1436 40.4 42.4
Somewhat_Disagree 820 23.1 24.2
Strongly_Disagree 629 17.7 18.6
Scantron_Error 8 0.2 0.2
NA 169 4.8 NA
Total 3557 100.0 100.0
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 429 12.1 12.6
Somewhat_Agree 1222 34.4 36.0
Somewhat_Disagree 1002 28.2 29.5
Strongly_Disagree 740 20.8 21.8
Scantron_Error 6 0.2 0.2
NA 158 4.4 NA
Total 3557 100.0 100.0
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 650 18.3 19.7
Somewhat_Agree 1104 31.0 33.5
Somewhat_Disagree 824 23.2 25.0
Strongly_Disagree 707 19.9 21.5
Scantron_Error 8 0.2 0.2
NA 264 7.4 NA
Total 3557 100.0 100.0
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 1841 51.8 54.7
Somewhat_Agree 900 25.3 26.7
Somewhat_Disagree 332 9.3 9.9
Strongly_Disagree 289 8.1 8.6
Scantron_Error 3 0.1 0.1
NA 192 5.4 NA
Total 3557 100.0 100.0
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 149 4.2 4.5
Somewhat_Agree 498 14.0 15.1
Somewhat_Disagree 1447 40.7 44.0
Strongly_Disagree 1195 33.6 36.3
Scantron_Error 0 0.0 0.0
NA 268 7.5 NA
Total 3557 100.0 100.0

LA County

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 49 15.3 15.7
Somewhat_Agree 139 43.3 44.6
Somewhat_Disagree 79 24.6 25.3
Strongly_Disagree 45 14.0 14.4
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 34 10.6 10.8
Somewhat_Agree 120 37.4 38.2
Somewhat_Disagree 100 31.2 31.8
Strongly_Disagree 60 18.7 19.1
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 81 25.2 26.2
Somewhat_Agree 101 31.5 32.7
Somewhat_Disagree 81 25.2 26.2
Strongly_Disagree 46 14.3 14.9
Scantron_Error 0 0.0 0.0
NA 12 3.7 NA
Total 321 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 179 55.8 57.0
Somewhat_Agree 95 29.6 30.3
Somewhat_Disagree 27 8.4 8.6
Strongly_Disagree 13 4.0 4.1
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 13 4.0 4.2
Somewhat_Agree 45 14.0 14.6
Somewhat_Disagree 154 48.0 50.0
Strongly_Disagree 96 29.9 31.2
Scantron_Error 0 0.0 0.0
NA 13 4.0 NA
Total 321 100.0 100.0

Northern CA

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 32 15.2 16
Somewhat_Agree 80 38.1 40
Somewhat_Disagree 46 21.9 23
Strongly_Disagree 42 20.0 21
Scantron_Error 0 0.0 0
NA 10 4.8 NA
Total 210 100.0 100
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 18 8.6 8.9
Somewhat_Agree 63 30.0 31.2
Somewhat_Disagree 61 29.0 30.2
Strongly_Disagree 60 28.6 29.7
Scantron_Error 0 0.0 0.0
NA 8 3.8 NA
Total 210 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 46 21.9 23.4
Somewhat_Agree 58 27.6 29.4
Somewhat_Disagree 38 18.1 19.3
Strongly_Disagree 55 26.2 27.9
Scantron_Error 0 0.0 0.0
NA 13 6.2 NA
Total 210 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 112 53.3 56.6
Somewhat_Agree 60 28.6 30.3
Somewhat_Disagree 13 6.2 6.6
Strongly_Disagree 13 6.2 6.6
Scantron_Error 0 0.0 0.0
NA 12 5.7 NA
Total 210 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 15 7.1 7.7
Somewhat_Agree 25 11.9 12.8
Somewhat_Disagree 84 40.0 43.1
Strongly_Disagree 71 33.8 36.4
Scantron_Error 0 0.0 0.0
NA 15 7.1 NA
Total 210 100.0 100.0

Greater CA

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 51 16.2 17.1
Somewhat_Agree 116 36.8 38.8
Somewhat_Disagree 71 22.5 23.7
Strongly_Disagree 61 19.4 20.4
Scantron_Error 0 0.0 0.0
NA 16 5.1 NA
Total 315 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 27 8.6 9.0
Somewhat_Agree 85 27.0 28.3
Somewhat_Disagree 112 35.6 37.3
Strongly_Disagree 76 24.1 25.3
Scantron_Error 0 0.0 0.0
NA 15 4.8 NA
Total 315 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 50 15.9 17.5
Somewhat_Agree 92 29.2 32.2
Somewhat_Disagree 71 22.5 24.8
Strongly_Disagree 73 23.2 25.5
Scantron_Error 0 0.0 0.0
NA 29 9.2 NA
Total 315 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 165 52.4 55.9
Somewhat_Agree 78 24.8 26.4
Somewhat_Disagree 30 9.5 10.2
Strongly_Disagree 21 6.7 7.1
Scantron_Error 1 0.3 0.3
NA 20 6.3 NA
Total 315 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 18 5.7 6.2
Somewhat_Agree 51 16.2 17.6
Somewhat_Disagree 130 41.3 44.8
Strongly_Disagree 91 28.9 31.4
Scantron_Error 0 0.0 0.0
NA 25 7.9 NA
Total 315 100.0 100.0

Detroit

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 46 12.9 13.8
Somewhat_Agree 131 36.8 39.2
Somewhat_Disagree 86 24.2 25.7
Strongly_Disagree 71 19.9 21.3
Scantron_Error 0 0.0 0.0
NA 22 6.2 NA
Total 356 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 38 10.7 11.3
Somewhat_Agree 125 35.1 37.2
Somewhat_Disagree 88 24.7 26.2
Strongly_Disagree 84 23.6 25.0
Scantron_Error 1 0.3 0.3
NA 20 5.6 NA
Total 356 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 56 15.7 17.4
Somewhat_Agree 107 30.1 33.3
Somewhat_Disagree 80 22.5 24.9
Strongly_Disagree 77 21.6 24.0
Scantron_Error 1 0.3 0.3
NA 35 9.8 NA
Total 356 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 171 48.0 51.7
Somewhat_Agree 90 25.3 27.2
Somewhat_Disagree 35 9.8 10.6
Strongly_Disagree 35 9.8 10.6
Scantron_Error 0 0.0 0.0
NA 25 7.0 NA
Total 356 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 18 5.1 5.6
Somewhat_Agree 47 13.2 14.7
Somewhat_Disagree 129 36.2 40.3
Strongly_Disagree 126 35.4 39.4
Scantron_Error 0 0.0 0.0
NA 36 10.1 NA
Total 356 100.0 100.0

Louisiana

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 82 14.0 14.8
Somewhat_Agree 223 38.1 40.3
Somewhat_Disagree 137 23.4 24.8
Strongly_Disagree 107 18.3 19.3
Scantron_Error 4 0.7 0.7
NA 32 5.5 NA
Total 585 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 78 13.3 14.2
Somewhat_Agree 199 34.0 36.1
Somewhat_Disagree 144 24.6 26.1
Strongly_Disagree 129 22.1 23.4
Scantron_Error 1 0.2 0.2
NA 34 5.8 NA
Total 585 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 102 17.4 19.3
Somewhat_Agree 165 28.2 31.2
Somewhat_Disagree 133 22.7 25.1
Strongly_Disagree 129 22.1 24.4
Scantron_Error 0 0.0 0.0
NA 56 9.6 NA
Total 585 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 275 47.0 49.5
Somewhat_Agree 152 26.0 27.4
Somewhat_Disagree 62 10.6 11.2
Strongly_Disagree 65 11.1 11.7
Scantron_Error 1 0.2 0.2
NA 30 5.1 NA
Total 585 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 22 3.8 4.1
Somewhat_Agree 77 13.2 14.3
Somewhat_Disagree 236 40.3 43.7
Strongly_Disagree 205 35.0 38.0
Scantron_Error 0 0.0 0.0
NA 45 7.7 NA
Total 585 100.0 100.0

Georgia

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 234 13.3 14.0
Somewhat_Agree 741 42.2 44.3
Somewhat_Disagree 398 22.7 23.8
Strongly_Disagree 297 16.9 17.7
Scantron_Error 4 0.2 0.2
NA 80 4.6 NA
Total 1754 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 233 13.3 13.9
Somewhat_Agree 625 35.6 37.2
Somewhat_Disagree 492 28.1 29.3
Strongly_Disagree 326 18.6 19.4
Scantron_Error 4 0.2 0.2
NA 74 4.2 NA
Total 1754 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 313 17.8 19.1
Somewhat_Agree 578 33.0 35.4
Somewhat_Disagree 417 23.8 25.5
Strongly_Disagree 321 18.3 19.6
Scantron_Error 6 0.3 0.4
NA 119 6.8 NA
Total 1754 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 933 53.2 56.3
Somewhat_Agree 419 23.9 25.3
Somewhat_Disagree 163 9.3 9.8
Strongly_Disagree 140 8.0 8.5
Scantron_Error 1 0.1 0.1
NA 98 5.6 NA
Total 1754 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 62 3.5 3.8
Somewhat_Agree 251 14.3 15.5
Somewhat_Disagree 708 40.4 43.7
Strongly_Disagree 599 34.2 37.0
Scantron_Error 0 0.0 0.0
NA 134 7.6 NA
Total 1754 100.0 100.0

Michigan

a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 1 6.2 6.2
Somewhat_Agree 6 37.5 37.5
Somewhat_Disagree 3 18.8 18.8
Strongly_Disagree 6 37.5 37.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 1 6.2 6.2
Somewhat_Agree 5 31.2 31.2
Somewhat_Disagree 5 31.2 31.2
Strongly_Disagree 5 31.2 31.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 2 12.5 12.5
Somewhat_Agree 3 18.8 18.8
Somewhat_Disagree 4 25.0 25.0
Strongly_Disagree 6 37.5 37.5
Scantron_Error 1 6.2 6.2
Total 16 100.0 100.0
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 6 37.5 37.5
Somewhat_Agree 6 37.5 37.5
Somewhat_Disagree 2 12.5 12.5
Strongly_Disagree 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 1 6.2 6.2
Somewhat_Agree 2 12.5 12.5
Somewhat_Disagree 6 37.5 37.5
Strongly_Disagree 7 43.8 43.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0

D3A

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3a1 <- as.factor(d[,"d3a1"])
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1048 29.5 30.2
Rarely 1014 28.5 29.2
Sometimes 1207 33.9 34.8
Often 198 5.6 5.7
Scantron_Error 2 0.1 0.1
NA 88 2.5 NA
Total 3557 100.0 100.0
#2
  d3a2 <- as.factor(d[,"d3a2"])
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 782 22.0 23.8
Rarely 957 26.9 29.1
Sometimes 1275 35.8 38.8
Often 269 7.6 8.2
Scantron_Error 2 0.1 0.1
NA 272 7.6 NA
Total 3557 100.0 100.0
#3
  d3a3 <- as.factor(d[,"d3a3"])
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 719 20.2 22.1
Rarely 717 20.2 22.0
Sometimes 1238 34.8 38.0
Often 576 16.2 17.7
Scantron_Error 4 0.1 0.1
NA 303 8.5 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 84 26.2 26.4
Rarely 100 31.2 31.4
Sometimes 116 36.1 36.5
Often 18 5.6 5.7
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 57 17.8 18.2
Rarely 89 27.7 28.4
Sometimes 146 45.5 46.6
Often 21 6.5 6.7
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 54 16.8 17.3
Rarely 72 22.4 23.0
Sometimes 142 44.2 45.4
Often 45 14.0 14.4
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 52 24.8 25.5
Rarely 56 26.7 27.5
Sometimes 75 35.7 36.8
Often 21 10.0 10.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 40 19.0 20.4
Rarely 59 28.1 30.1
Sometimes 74 35.2 37.8
Often 23 11.0 11.7
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 41 19.5 21.1
Rarely 45 21.4 23.2
Sometimes 66 31.4 34.0
Often 42 20.0 21.6
Scantron_Error 0 0.0 0.0
NA 16 7.6 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 65 20.6 21.5
Rarely 89 28.3 29.5
Sometimes 122 38.7 40.4
Often 26 8.3 8.6
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 45 14.3 15.5
Rarely 79 25.1 27.2
Sometimes 129 41.0 44.5
Often 37 11.7 12.8
Scantron_Error 0 0.0 0.0
NA 25 7.9 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 48 15.2 16.5
Rarely 59 18.7 20.3
Sometimes 116 36.8 39.9
Often 67 21.3 23.0
Scantron_Error 1 0.3 0.3
NA 24 7.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 111 31.2 31.6
Rarely 122 34.3 34.8
Sometimes 103 28.9 29.3
Often 15 4.2 4.3
Scantron_Error 0 0.0 0.0
NA 5 1.4 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 81 22.8 25.2
Rarely 117 32.9 36.3
Sometimes 104 29.2 32.3
Often 20 5.6 6.2
Scantron_Error 0 0.0 0.0
NA 34 9.6 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 78 21.9 24.5
Rarely 87 24.4 27.3
Sometimes 117 32.9 36.7
Often 37 10.4 11.6
Scantron_Error 0 0.0 0.0
NA 37 10.4 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 185 31.6 32.3
Rarely 141 24.1 24.7
Sometimes 206 35.2 36.0
Often 39 6.7 6.8
Scantron_Error 1 0.2 0.2
NA 13 2.2 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 144 24.6 27.5
Rarely 132 22.6 25.2
Sometimes 195 33.3 37.2
Often 52 8.9 9.9
Scantron_Error 1 0.2 0.2
NA 61 10.4 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 131 22.4 25.1
Rarely 95 16.2 18.2
Sometimes 202 34.5 38.7
Often 93 15.9 17.8
Scantron_Error 1 0.2 0.2
NA 63 10.8 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 545 31.1 31.9
Rarely 501 28.6 29.4
Sometimes 580 33.1 34.0
Often 79 4.5 4.6
Scantron_Error 1 0.1 0.1
NA 48 2.7 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 410 23.4 25.2
Rarely 478 27.3 29.4
Sometimes 620 35.3 38.2
Often 116 6.6 7.1
Scantron_Error 1 0.1 0.1
NA 129 7.4 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 366 20.9 22.9
Rarely 351 20.0 21.9
Sometimes 591 33.7 36.9
Often 291 16.6 18.2
Scantron_Error 1 0.1 0.1
NA 154 8.8 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 6 37.5 37.5
Rarely 5 31.2 31.2
Sometimes 5 31.2 31.2
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 5 31.2 33.3
Rarely 3 18.8 20.0
Sometimes 7 43.8 46.7
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1 6.2 6.7
Rarely 8 50.0 53.3
Sometimes 4 25.0 26.7
Often 1 6.2 6.7
Scantron_Error 1 6.2 6.7
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3B

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3b1 <- as.factor(d[,"d3b1"])
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 795 22.4 23.1
Rarely 1124 31.6 32.7
Sometimes 1344 37.8 39.0
Often 178 5.0 5.2
Scantron_Error 1 0.0 0.0
NA 115 3.2 NA
Total 3557 100.0 100.0
#2
  d3b2 <- as.factor(d[,"d3b2"])
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 601 16.9 18.4
Rarely 978 27.5 30.0
Sometimes 1451 40.8 44.5
Often 229 6.4 7.0
Scantron_Error 0 0.0 0.0
NA 298 8.4 NA
Total 3557 100.0 100.0
#3
  d3b3 <- as.factor(d[,"d3b3"])
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 603 17.0 18.7
Rarely 728 20.5 22.6
Sometimes 1332 37.4 41.4
Often 556 15.6 17.3
Scantron_Error 0 0.0 0.0
NA 338 9.5 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 64 19.9 20.2
Rarely 116 36.1 36.6
Sometimes 119 37.1 37.5
Often 18 5.6 5.7
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 46 14.3 14.8
Rarely 102 31.8 32.8
Sometimes 144 44.9 46.3
Often 19 5.9 6.1
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 40 12.5 12.9
Rarely 95 29.6 30.6
Sometimes 142 44.2 45.8
Often 33 10.3 10.6
Scantron_Error 0 0.0 0.0
NA 11 3.4 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 35 16.7 17.2
Rarely 66 31.4 32.4
Sometimes 84 40.0 41.2
Often 19 9.0 9.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 29 13.8 14.9
Rarely 58 27.6 29.9
Sometimes 84 40.0 43.3
Often 23 11.0 11.9
Scantron_Error 0 0.0 0.0
NA 16 7.6 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 32 15.2 16.8
Rarely 43 20.5 22.5
Sometimes 85 40.5 44.5
Often 31 14.8 16.2
Scantron_Error 0 0.0 0.0
NA 19 9.0 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 66 21.0 22.0
Rarely 94 29.8 31.3
Sometimes 121 38.4 40.3
Often 18 5.7 6.0
Scantron_Error 1 0.3 0.3
NA 15 4.8 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 48 15.2 16.5
Rarely 85 27.0 29.2
Sometimes 135 42.9 46.4
Often 23 7.3 7.9
Scantron_Error 0 0.0 0.0
NA 24 7.6 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 51 16.2 17.7
Rarely 56 17.8 19.4
Sometimes 122 38.7 42.4
Often 59 18.7 20.5
Scantron_Error 0 0.0 0.0
NA 27 8.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 88 24.7 25.1
Rarely 123 34.6 35.1
Sometimes 119 33.4 34.0
Often 20 5.6 5.7
Scantron_Error 0 0.0 0.0
NA 6 1.7 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 63 17.7 19.6
Rarely 112 31.5 34.8
Sometimes 126 35.4 39.1
Often 21 5.9 6.5
Scantron_Error 0 0.0 0.0
NA 34 9.6 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 63 17.7 19.9
Rarely 85 23.9 26.9
Sometimes 133 37.4 42.1
Often 35 9.8 11.1
Scantron_Error 0 0.0 0.0
NA 40 11.2 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 160 27.4 28.6
Rarely 153 26.2 27.4
Sometimes 216 36.9 38.6
Often 30 5.1 5.4
Scantron_Error 0 0.0 0.0
NA 26 4.4 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 111 19.0 21.7
Rarely 131 22.4 25.6
Sometimes 225 38.5 44.0
Often 44 7.5 8.6
Scantron_Error 0 0.0 0.0
NA 74 12.6 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 105 17.9 20.5
Rarely 91 15.6 17.8
Sometimes 212 36.2 41.4
Often 104 17.8 20.3
Scantron_Error 0 0.0 0.0
NA 73 12.5 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 376 21.4 22.2
Rarely 568 32.4 33.5
Sometimes 681 38.8 40.2
Often 71 4.0 4.2
Scantron_Error 0 0.0 0.0
NA 58 3.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 298 17.0 18.5
Rarely 486 27.7 30.1
Sometimes 734 41.8 45.4
Often 97 5.5 6.0
Scantron_Error 0 0.0 0.0
NA 139 7.9 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 308 17.6 19.4
Rarely 354 20.2 22.3
Sometimes 634 36.1 39.9
Often 291 16.6 18.3
Scantron_Error 0 0.0 0.0
NA 167 9.5 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 6 37.5 37.5
Rarely 4 25.0 25.0
Sometimes 4 25.0 25.0
Often 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 6 37.5 40.0
Rarely 4 25.0 26.7
Sometimes 3 18.8 20.0
Often 2 12.5 13.3
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 4 25.0 26.7
Rarely 4 25.0 26.7
Sometimes 4 25.0 26.7
Often 3 18.8 20.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3C

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3c1 <- as.factor(d[,"d3c1"])
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 968 27.2 28.3
Rarely 1055 29.7 30.9
Sometimes 1118 31.4 32.7
Often 276 7.8 8.1
Scantron_Error 1 0.0 0.0
NA 139 3.9 NA
Total 3557 100.0 100.0
#2
  d3c2 <- as.factor(d[,"d3c2"])
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 788 22.2 24.3
Rarely 1016 28.6 31.3
Sometimes 1154 32.4 35.6
Often 286 8.0 8.8
Scantron_Error 0 0.0 0.0
NA 313 8.8 NA
Total 3557 100.0 100.0
#3
  d3c3 <- as.factor(d[,"d3c3"])
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 725 20.4 22.5
Rarely 812 22.8 25.2
Sometimes 1194 33.6 37.1
Often 486 13.7 15.1
Scantron_Error 0 0.0 0.0
NA 340 9.6 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 72 22.4 22.9
Rarely 113 35.2 36.0
Sometimes 103 32.1 32.8
Often 26 8.1 8.3
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 57 17.8 18.6
Rarely 97 30.2 31.6
Sometimes 128 39.9 41.7
Often 25 7.8 8.1
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 52 16.2 17.0
Rarely 82 25.5 26.8
Sometimes 135 42.1 44.1
Often 37 11.5 12.1
Scantron_Error 0 0.0 0.0
NA 15 4.7 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 42 20.0 20.4
Rarely 72 34.3 35.0
Sometimes 70 33.3 34.0
Often 22 10.5 10.7
Scantron_Error 0 0.0 0.0
NA 4 1.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 41 19.5 20.8
Rarely 64 30.5 32.5
Sometimes 69 32.9 35.0
Often 23 11.0 11.7
Scantron_Error 0 0.0 0.0
NA 13 6.2 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 39 18.6 20.0
Rarely 52 24.8 26.7
Sometimes 69 32.9 35.4
Often 35 16.7 17.9
Scantron_Error 0 0.0 0.0
NA 15 7.1 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 79 25.1 26.6
Rarely 88 27.9 29.6
Sometimes 105 33.3 35.4
Often 25 7.9 8.4
Scantron_Error 0 0.0 0.0
NA 18 5.7 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 61 19.4 21.3
Rarely 76 24.1 26.5
Sometimes 120 38.1 41.8
Often 30 9.5 10.5
Scantron_Error 0 0.0 0.0
NA 28 8.9 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 60 19.0 21.1
Rarely 63 20.0 22.1
Sometimes 106 33.7 37.2
Often 56 17.8 19.6
Scantron_Error 0 0.0 0.0
NA 30 9.5 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 106 29.8 31.0
Rarely 106 29.8 31.0
Sometimes 108 30.3 31.6
Often 22 6.2 6.4
Scantron_Error 0 0.0 0.0
NA 14 3.9 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 85 23.9 26.6
Rarely 103 28.9 32.2
Sometimes 104 29.2 32.5
Often 28 7.9 8.8
Scantron_Error 0 0.0 0.0
NA 36 10.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 80 22.5 25.5
Rarely 87 24.4 27.7
Sometimes 107 30.1 34.1
Often 40 11.2 12.7
Scantron_Error 0 0.0 0.0
NA 42 11.8 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 165 28.2 29.6
Rarely 141 24.1 25.3
Sometimes 200 34.2 35.9
Often 51 8.7 9.2
Scantron_Error 0 0.0 0.0
NA 28 4.8 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 138 23.6 26.6
Rarely 141 24.1 27.2
Sometimes 188 32.1 36.3
Often 51 8.7 9.8
Scantron_Error 0 0.0 0.0
NA 67 11.5 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 126 21.5 24.4
Rarely 122 20.9 23.6
Sometimes 180 30.8 34.8
Often 89 15.2 17.2
Scantron_Error 0 0.0 0.0
NA 68 11.6 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 499 28.4 29.6
Rarely 531 30.3 31.5
Sometimes 527 30.0 31.2
Often 129 7.4 7.6
Scantron_Error 1 0.1 0.1
NA 67 3.8 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 402 22.9 25.1
Rarely 530 30.2 33.1
Sometimes 543 31.0 33.9
Often 127 7.2 7.9
Scantron_Error 0 0.0 0.0
NA 152 8.7 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 363 20.7 22.9
Rarely 403 23.0 25.4
Sometimes 594 33.9 37.4
Often 227 12.9 14.3
Scantron_Error 0 0.0 0.0
NA 167 9.5 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 5 31.2 33.3
Rarely 4 25.0 26.7
Sometimes 5 31.2 33.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 4 25.0 30.8
Rarely 5 31.2 38.5
Sometimes 2 12.5 15.4
Often 2 12.5 15.4
Scantron_Error 0 0.0 0.0
NA 3 18.8 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 5 31.2 38.5
Rarely 3 18.8 23.1
Sometimes 3 18.8 23.1
Often 2 12.5 15.4
Scantron_Error 0 0.0 0.0
NA 3 18.8 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3D

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3d1 <- as.factor(d[,"d3d1"])
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1289 36.2 37.4
Rarely 961 27.0 27.9
Sometimes 974 27.4 28.3
Often 218 6.1 6.3
Scantron_Error 0 0.0 0.0
NA 115 3.2 NA
Total 3557 100.0 100.0
#2
  d3d2 <- as.factor(d[,"d3d2"])
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1025 28.8 31.5
Rarely 874 24.6 26.8
Sometimes 1064 29.9 32.7
Often 294 8.3 9.0
Scantron_Error 0 0.0 0.0
NA 300 8.4 NA
Total 3557 100.0 100.0
#3
  d3d3 <- as.factor(d[,"d3d3"])
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1096 30.8 33.9
Rarely 795 22.4 24.6
Sometimes 961 27.0 29.7
Often 380 10.7 11.8
Scantron_Error 1 0.0 0.0
NA 324 9.1 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 90 28.0 28.8
Rarely 88 27.4 28.1
Sometimes 106 33.0 33.9
Often 29 9.0 9.3
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 71 22.1 23.0
Rarely 74 23.1 23.9
Sometimes 130 40.5 42.1
Often 34 10.6 11.0
Scantron_Error 0 0.0 0.0
NA 12 3.7 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 75 23.4 24.4
Rarely 73 22.7 23.7
Sometimes 119 37.1 38.6
Often 41 12.8 13.3
Scantron_Error 0 0.0 0.0
NA 13 4.0 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 41 19.5 20.1
Rarely 63 30.0 30.9
Sometimes 77 36.7 37.7
Often 23 11.0 11.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 34 16.2 17.3
Rarely 54 25.7 27.6
Sometimes 76 36.2 38.8
Often 32 15.2 16.3
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 42 20.0 21.6
Rarely 51 24.3 26.3
Sometimes 69 32.9 35.6
Often 32 15.2 16.5
Scantron_Error 0 0.0 0.0
NA 16 7.6 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 81 25.7 26.8
Rarely 93 29.5 30.8
Sometimes 101 32.1 33.4
Often 27 8.6 8.9
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 67 21.3 23.1
Rarely 75 23.8 25.9
Sometimes 106 33.7 36.6
Often 42 13.3 14.5
Scantron_Error 0 0.0 0.0
NA 25 7.9 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 74 23.5 25.7
Rarely 65 20.6 22.6
Sometimes 101 32.1 35.1
Often 48 15.2 16.7
Scantron_Error 0 0.0 0.0
NA 27 8.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 127 35.7 37.0
Rarely 101 28.4 29.4
Sometimes 93 26.1 27.1
Often 22 6.2 6.4
Scantron_Error 0 0.0 0.0
NA 13 3.7 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 100 28.1 30.9
Rarely 88 24.7 27.2
Sometimes 107 30.1 33.0
Often 29 8.1 9.0
Scantron_Error 0 0.0 0.0
NA 32 9.0 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 111 31.2 34.9
Rarely 84 23.6 26.4
Sometimes 86 24.2 27.0
Often 37 10.4 11.6
Scantron_Error 0 0.0 0.0
NA 38 10.7 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 247 42.2 44.1
Rarely 135 23.1 24.1
Sometimes 155 26.5 27.7
Often 23 3.9 4.1
Scantron_Error 0 0.0 0.0
NA 25 4.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 185 31.6 36.1
Rarely 137 23.4 26.7
Sometimes 152 26.0 29.6
Often 39 6.7 7.6
Scantron_Error 0 0.0 0.0
NA 72 12.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 197 33.7 38.2
Rarely 118 20.2 22.9
Sometimes 147 25.1 28.5
Often 54 9.2 10.5
Scantron_Error 0 0.0 0.0
NA 69 11.8 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 699 39.9 41.0
Rarely 478 27.3 28.1
Sometimes 436 24.9 25.6
Often 91 5.2 5.3
Scantron_Error 0 0.0 0.0
NA 50 2.9 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 564 32.2 35.0
Rarely 442 25.2 27.5
Sometimes 489 27.9 30.4
Often 115 6.6 7.1
Scantron_Error 0 0.0 0.0
NA 144 8.2 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 593 33.8 37.2
Rarely 400 22.8 25.1
Sometimes 436 24.9 27.4
Often 164 9.4 10.3
Scantron_Error 1 0.1 0.1
NA 160 9.1 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 4 25.0 25.0
Rarely 3 18.8 18.8
Sometimes 6 37.5 37.5
Often 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 4 25.0 26.7
Rarely 4 25.0 26.7
Sometimes 4 25.0 26.7
Often 3 18.8 20.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 4 25.0 26.7
Rarely 4 25.0 26.7
Sometimes 3 18.8 20.0
Often 4 25.0 26.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3E

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3e1 <- as.factor(d[,"d3e1"])
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1498 42.1 43.5
Rarely 1001 28.1 29.1
Sometimes 765 21.5 22.2
Often 174 4.9 5.1
Scantron_Error 3 0.1 0.1
NA 116 3.3 NA
Total 3557 100.0 100.0
#2
  d3e2 <- as.factor(d[,"d3e2"])
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1207 33.9 37.1
Rarely 951 26.7 29.2
Sometimes 885 24.9 27.2
Often 211 5.9 6.5
Scantron_Error 1 0.0 0.0
NA 302 8.5 NA
Total 3557 100.0 100.0
#3
  d3e3 <- as.factor(d[,"d3e3"])
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1179 33.1 36.5
Rarely 793 22.3 24.5
Sometimes 904 25.4 28.0
Often 356 10.0 11.0
Scantron_Error 1 0.0 0.0
NA 324 9.1 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 116 36.1 36.7
Rarely 103 32.1 32.6
Sometimes 78 24.3 24.7
Often 19 5.9 6.0
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 89 27.7 28.5
Rarely 106 33.0 34.0
Sometimes 95 29.6 30.4
Often 22 6.9 7.1
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 86 26.8 27.6
Rarely 85 26.5 27.2
Sometimes 111 34.6 35.6
Often 30 9.3 9.6
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 67 31.9 32.7
Rarely 62 29.5 30.2
Sometimes 58 27.6 28.3
Often 18 8.6 8.8
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 57 27.1 29.1
Rarely 54 25.7 27.6
Sometimes 65 31.0 33.2
Often 20 9.5 10.2
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 53 25.2 27.2
Rarely 51 24.3 26.2
Sometimes 65 31.0 33.3
Often 26 12.4 13.3
Scantron_Error 0 0.0 0.0
NA 15 7.1 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 118 37.5 39.2
Rarely 94 29.8 31.2
Sometimes 74 23.5 24.6
Often 15 4.8 5.0
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 88 27.9 30.4
Rarely 87 27.6 30.1
Sometimes 89 28.3 30.8
Often 25 7.9 8.7
Scantron_Error 0 0.0 0.0
NA 26 8.3 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 83 26.3 28.9
Rarely 75 23.8 26.1
Sometimes 93 29.5 32.4
Often 36 11.4 12.5
Scantron_Error 0 0.0 0.0
NA 28 8.9 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 144 40.4 42.0
Rarely 103 28.9 30.0
Sometimes 77 21.6 22.4
Often 19 5.3 5.5
Scantron_Error 0 0.0 0.0
NA 13 3.7 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 115 32.3 36.1
Rarely 93 26.1 29.2
Sometimes 89 25.0 27.9
Often 22 6.2 6.9
Scantron_Error 0 0.0 0.0
NA 37 10.4 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 113 31.7 35.9
Rarely 78 21.9 24.8
Sometimes 93 26.1 29.5
Often 31 8.7 9.8
Scantron_Error 0 0.0 0.0
NA 41 11.5 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 262 44.8 46.8
Rarely 136 23.2 24.3
Sometimes 132 22.6 23.6
Often 29 5.0 5.2
Scantron_Error 1 0.2 0.2
NA 25 4.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 208 35.6 40.4
Rarely 131 22.4 25.4
Sometimes 145 24.8 28.2
Often 31 5.3 6.0
Scantron_Error 0 0.0 0.0
NA 70 12.0 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 209 35.7 40.4
Rarely 106 18.1 20.5
Sometimes 139 23.8 26.9
Often 62 10.6 12.0
Scantron_Error 1 0.2 0.2
NA 68 11.6 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 787 44.9 46.3
Rarely 497 28.3 29.2
Sometimes 341 19.4 20.0
Often 74 4.2 4.4
Scantron_Error 2 0.1 0.1
NA 53 3.0 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 646 36.8 40.1
Rarely 475 27.1 29.5
Sometimes 397 22.6 24.7
Often 90 5.1 5.6
Scantron_Error 1 0.1 0.1
NA 145 8.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 630 35.9 39.6
Rarely 394 22.5 24.7
Sometimes 398 22.7 25.0
Often 170 9.7 10.7
Scantron_Error 0 0.0 0.0
NA 162 9.2 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 4 25.0 26.7
Rarely 6 37.5 40.0
Sometimes 5 31.2 33.3
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 4 25.0 26.7
Rarely 5 31.2 33.3
Sometimes 5 31.2 33.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 5 31.2 33.3
Rarely 4 25.0 26.7
Sometimes 5 31.2 33.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3F

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3f1 <- as.factor(d[,"d3f1"])
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 550 15.5 16.0
Rarely 948 26.7 27.6
Sometimes 1500 42.2 43.6
Often 441 12.4 12.8
Scantron_Error 1 0.0 0.0
NA 117 3.3 NA
Total 3557 100.0 100.0
#2
  d3f2 <- as.factor(d[,"d3f2"])
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 408 11.5 12.5
Rarely 836 23.5 25.6
Sometimes 1553 43.7 47.6
Often 468 13.2 14.3
Scantron_Error 1 0.0 0.0
NA 291 8.2 NA
Total 3557 100.0 100.0
#3
  d3f3 <- as.factor(d[,"d3f3"])
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 413 11.6 12.7
Rarely 664 18.7 20.5
Sometimes 1415 39.8 43.6
Often 747 21.0 23.0
Scantron_Error 4 0.1 0.1
NA 314 8.8 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 44 13.7 14.0
Rarely 95 29.6 30.2
Sometimes 133 41.4 42.2
Often 43 13.4 13.7
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 38 11.8 12.2
Rarely 82 25.5 26.3
Sometimes 143 44.5 45.8
Often 49 15.3 15.7
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 42 13.1 13.5
Rarely 69 21.5 22.3
Sometimes 142 44.2 45.8
Often 57 17.8 18.4
Scantron_Error 0 0.0 0.0
NA 11 3.4 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 24 11.4 11.7
Rarely 52 24.8 25.4
Sometimes 97 46.2 47.3
Often 32 15.2 15.6
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 23 11.0 11.7
Rarely 49 23.3 25.0
Sometimes 91 43.3 46.4
Often 33 15.7 16.8
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 23 11.0 11.8
Rarely 33 15.7 16.9
Sometimes 95 45.2 48.7
Often 44 21.0 22.6
Scantron_Error 0 0.0 0.0
NA 15 7.1 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 44 14.0 14.7
Rarely 79 25.1 26.4
Sometimes 128 40.6 42.8
Often 48 15.2 16.1
Scantron_Error 0 0.0 0.0
NA 16 5.1 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 32 10.2 11.1
Rarely 63 20.0 21.8
Sometimes 139 44.1 48.1
Often 55 17.5 19.0
Scantron_Error 0 0.0 0.0
NA 26 8.3 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 30 9.5 10.5
Rarely 64 20.3 22.5
Sometimes 111 35.2 38.9
Often 80 25.4 28.1
Scantron_Error 0 0.0 0.0
NA 30 9.5 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 58 16.3 17.0
Rarely 91 25.6 26.6
Sometimes 150 42.1 43.9
Often 43 12.1 12.6
Scantron_Error 0 0.0 0.0
NA 14 3.9 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 36 10.1 11.4
Rarely 88 24.7 27.8
Sometimes 146 41.0 46.2
Often 46 12.9 14.6
Scantron_Error 0 0.0 0.0
NA 40 11.2 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 42 11.8 13.4
Rarely 80 22.5 25.6
Sometimes 125 35.1 39.9
Often 66 18.5 21.1
Scantron_Error 0 0.0 0.0
NA 43 12.1 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 91 15.6 16.1
Rarely 126 21.5 22.3
Sometimes 258 44.1 45.6
Often 90 15.4 15.9
Scantron_Error 1 0.2 0.2
NA 19 3.2 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 10.6 11.9
Rarely 114 19.5 21.8
Sometimes 263 45.0 50.4
Often 82 14.0 15.7
Scantron_Error 1 0.2 0.2
NA 63 10.8 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 68 11.6 12.9
Rarely 93 15.9 17.7
Sometimes 230 39.3 43.7
Often 133 22.7 25.3
Scantron_Error 2 0.3 0.4
NA 59 10.1 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 284 16.2 16.7
Rarely 503 28.7 29.6
Sometimes 726 41.4 42.8
Often 184 10.5 10.8
Scantron_Error 0 0.0 0.0
NA 57 3.2 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 214 12.2 13.2
Rarely 437 24.9 27.0
Sometimes 763 43.5 47.2
Often 202 11.5 12.5
Scantron_Error 0 0.0 0.0
NA 138 7.9 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 204 11.6 12.8
Rarely 322 18.4 20.1
Sometimes 708 40.4 44.3
Often 363 20.7 22.7
Scantron_Error 2 0.1 0.1
NA 155 8.8 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 5 31.2 31.2
Rarely 2 12.5 12.5
Sometimes 8 50.0 50.0
Often 1 6.2 6.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 3 18.8 20.0
Rarely 3 18.8 20.0
Sometimes 8 50.0 53.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 4 25.0 26.7
Rarely 3 18.8 20.0
Sometimes 4 25.0 26.7
Often 4 25.0 26.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3G

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3g1 <- as.factor(d[,"d3g1"])
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1247 35.1 36.3
Rarely 1180 33.2 34.4
Sometimes 869 24.4 25.3
Often 136 3.8 4.0
Scantron_Error 2 0.1 0.1
NA 123 3.5 NA
Total 3557 100.0 100.0
#2
  d3g2 <- as.factor(d[,"d3g2"])
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 815 22.9 25.0
Rarely 1177 33.1 36.1
Sometimes 1099 30.9 33.7
Often 171 4.8 5.2
Scantron_Error 0 0.0 0.0
NA 295 8.3 NA
Total 3557 100.0 100.0
#3
  d3g3 <- as.factor(d[,"d3g3"])
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 566 15.9 17.5
Rarely 836 23.5 25.8
Sometimes 1339 37.6 41.3
Often 497 14.0 15.3
Scantron_Error 3 0.1 0.1
NA 316 8.9 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 125 38.9 39.7
Rarely 121 37.7 38.4
Sometimes 62 19.3 19.7
Often 7 2.2 2.2
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 19.3 19.9
Rarely 137 42.7 44.1
Sometimes 103 32.1 33.1
Often 9 2.8 2.9
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 32 10.0 10.3
Rarely 104 32.4 33.3
Sometimes 144 44.9 46.2
Often 32 10.0 10.3
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 50 23.8 24.4
Rarely 83 39.5 40.5
Sometimes 64 30.5 31.2
Often 8 3.8 3.9
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 32 15.2 16.3
Rarely 82 39.0 41.8
Sometimes 68 32.4 34.7
Often 14 6.7 7.1
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 30 14.3 15.3
Rarely 46 21.9 23.5
Sometimes 87 41.4 44.4
Often 33 15.7 16.8
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 96 30.5 31.8
Rarely 124 39.4 41.1
Sometimes 73 23.2 24.2
Often 9 2.9 3.0
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 19.7 21.2
Rarely 108 34.3 36.9
Sometimes 106 33.7 36.2
Often 17 5.4 5.8
Scantron_Error 0 0.0 0.0
NA 22 7.0 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 44 14.0 15.1
Rarely 61 19.4 21.0
Sometimes 130 41.3 44.7
Often 56 17.8 19.2
Scantron_Error 0 0.0 0.0
NA 24 7.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 114 32.0 33.1
Rarely 111 31.2 32.3
Sometimes 102 28.7 29.7
Often 17 4.8 4.9
Scantron_Error 0 0.0 0.0
NA 12 3.4 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 80 22.5 24.8
Rarely 112 31.5 34.7
Sometimes 114 32.0 35.3
Often 17 4.8 5.3
Scantron_Error 0 0.0 0.0
NA 33 9.3 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 64 18.0 20.2
Rarely 78 21.9 24.6
Sometimes 136 38.2 42.9
Often 39 11.0 12.3
Scantron_Error 0 0.0 0.0
NA 39 11.0 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 189 32.3 34.0
Rarely 167 28.5 30.0
Sometimes 166 28.4 29.9
Often 34 5.8 6.1
Scantron_Error 0 0.0 0.0
NA 29 5.0 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 122 20.9 23.7
Rarely 158 27.0 30.7
Sometimes 195 33.3 37.9
Often 39 6.7 7.6
Scantron_Error 0 0.0 0.0
NA 71 12.1 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 92 15.7 17.9
Rarely 106 18.1 20.6
Sometimes 221 37.8 42.9
Often 96 16.4 18.6
Scantron_Error 0 0.0 0.0
NA 70 12.0 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 669 38.1 39.4
Rarely 568 32.4 33.5
Sometimes 396 22.6 23.3
Often 61 3.5 3.6
Scantron_Error 2 0.1 0.1
NA 58 3.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 453 25.8 28.1
Rarely 578 33.0 35.9
Sometimes 505 28.8 31.4
Often 74 4.2 4.6
Scantron_Error 0 0.0 0.0
NA 144 8.2 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 301 17.2 18.9
Rarely 441 25.1 27.6
Sometimes 612 34.9 38.4
Often 239 13.6 15.0
Scantron_Error 2 0.1 0.1
NA 159 9.1 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 4 25.0 25.0
Rarely 6 37.5 37.5
Sometimes 6 37.5 37.5
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 4 25.0 26.7
Rarely 2 12.5 13.3
Sometimes 8 50.0 53.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 3 18.8 20.0
Rarely 0 0.0 0.0
Sometimes 9 56.2 60.0
Often 2 12.5 13.3
Scantron_Error 1 6.2 6.7
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3H

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3h1 <- as.factor(d[,"d3h1"])
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1905 53.6 55.6
Rarely 1002 28.2 29.2
Sometimes 466 13.1 13.6
Often 52 1.5 1.5
Scantron_Error 2 0.1 0.1
NA 130 3.7 NA
Total 3557 100.0 100.0
#2
  d3h2 <- as.factor(d[,"d3h2"])
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1394 39.2 42.8
Rarely 1135 31.9 34.9
Sometimes 630 17.7 19.4
Often 92 2.6 2.8
Scantron_Error 3 0.1 0.1
NA 303 8.5 NA
Total 3557 100.0 100.0
#3
  d3h3 <- as.factor(d[,"d3h3"])
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1089 30.6 33.6
Rarely 944 26.5 29.1
Sometimes 931 26.2 28.7
Often 273 7.7 8.4
Scantron_Error 3 0.1 0.1
NA 317 8.9 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 166 51.7 52.9
Rarely 106 33.0 33.8
Sometimes 38 11.8 12.1
Often 4 1.2 1.3
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 96 29.9 30.9
Rarely 143 44.5 46.0
Sometimes 62 19.3 19.9
Often 10 3.1 3.2
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 69 21.5 22.2
Rarely 115 35.8 37.0
Sometimes 109 34.0 35.0
Often 18 5.6 5.8
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 98 46.7 47.8
Rarely 68 32.4 33.2
Sometimes 34 16.2 16.6
Often 5 2.4 2.4
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 73 34.8 37.1
Rarely 74 35.2 37.6
Sometimes 41 19.5 20.8
Often 9 4.3 4.6
Scantron_Error 0 0.0 0.0
NA 13 6.2 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 58 27.6 29.6
Rarely 57 27.1 29.1
Sometimes 63 30.0 32.1
Often 18 8.6 9.2
Scantron_Error 0 0.0 0.0
NA 14 6.7 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 155 49.2 51.5
Rarely 102 32.4 33.9
Sometimes 37 11.7 12.3
Often 6 1.9 2.0
Scantron_Error 1 0.3 0.3
NA 14 4.4 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 112 35.6 38.4
Rarely 109 34.6 37.3
Sometimes 62 19.7 21.2
Often 8 2.5 2.7
Scantron_Error 1 0.3 0.3
NA 23 7.3 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 79 25.1 27.2
Rarely 81 25.7 27.9
Sometimes 94 29.8 32.4
Often 35 11.1 12.1
Scantron_Error 1 0.3 0.3
NA 25 7.9 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 176 49.4 52.1
Rarely 107 30.1 31.7
Sometimes 50 14.0 14.8
Often 5 1.4 1.5
Scantron_Error 0 0.0 0.0
NA 18 5.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 128 36.0 40.6
Rarely 117 32.9 37.1
Sometimes 59 16.6 18.7
Often 11 3.1 3.5
Scantron_Error 0 0.0 0.0
NA 41 11.5 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 105 29.5 33.5
Rarely 98 27.5 31.3
Sometimes 90 25.3 28.8
Often 20 5.6 6.4
Scantron_Error 0 0.0 0.0
NA 43 12.1 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 317 54.2 56.2
Rarely 149 25.5 26.4
Sometimes 86 14.7 15.2
Often 12 2.1 2.1
Scantron_Error 0 0.0 0.0
NA 21 3.6 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 242 41.4 46.4
Rarely 152 26.0 29.1
Sometimes 108 18.5 20.7
Often 20 3.4 3.8
Scantron_Error 0 0.0 0.0
NA 63 10.8 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 190 32.5 36.5
Rarely 146 25.0 28.0
Sometimes 134 22.9 25.7
Often 51 8.7 9.8
Scantron_Error 0 0.0 0.0
NA 64 10.9 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 984 56.1 58.2
Rarely 466 26.6 27.6
Sometimes 219 12.5 13.0
Often 20 1.1 1.2
Scantron_Error 1 0.1 0.1
NA 64 3.6 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 734 41.8 45.8
Rarely 538 30.7 33.6
Sometimes 295 16.8 18.4
Often 34 1.9 2.1
Scantron_Error 2 0.1 0.1
NA 151 8.6 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 581 33.1 36.4
Rarely 446 25.4 28.0
Sometimes 435 24.8 27.3
Often 131 7.5 8.2
Scantron_Error 2 0.1 0.1
NA 159 9.1 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 9 56.2 60.0
Rarely 4 25.0 26.7
Sometimes 2 12.5 13.3
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 9 56.2 64.3
Rarely 2 12.5 14.3
Sometimes 3 18.8 21.4
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 2 12.5 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 7 43.8 50.0
Rarely 1 6.2 7.1
Sometimes 6 37.5 42.9
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 2 12.5 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3I

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3i1 <- as.factor(d[,"d3i1"])
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1294 36.4 37.7
Rarely 952 26.8 27.8
Sometimes 932 26.2 27.2
Often 249 7.0 7.3
Scantron_Error 2 0.1 0.1
NA 128 3.6 NA
Total 3557 100.0 100.0
#2
  d3i2 <- as.factor(d[,"d3i2"])
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 917 25.8 28.2
Rarely 888 25.0 27.3
Sometimes 1111 31.2 34.1
Often 338 9.5 10.4
Scantron_Error 1 0.0 0.0
NA 302 8.5 NA
Total 3557 100.0 100.0
#3
  d3i3 <- as.factor(d[,"d3i3"])
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 773 21.7 23.9
Rarely 635 17.9 19.6
Sometimes 1133 31.9 35.0
Often 692 19.5 21.4
Scantron_Error 3 0.1 0.1
NA 321 9.0 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 116 36.1 36.7
Rarely 80 24.9 25.3
Sometimes 90 28.0 28.5
Often 30 9.3 9.5
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 74 23.1 23.8
Rarely 93 29.0 29.9
Sometimes 108 33.6 34.7
Often 36 11.2 11.6
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 56 17.4 17.9
Rarely 75 23.4 24.0
Sometimes 126 39.3 40.4
Often 55 17.1 17.6
Scantron_Error 0 0.0 0.0
NA 9 2.8 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 53 25.2 25.9
Rarely 61 29.0 29.8
Sometimes 75 35.7 36.6
Often 16 7.6 7.8
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 39 18.6 19.8
Rarely 48 22.9 24.4
Sometimes 82 39.0 41.6
Often 28 13.3 14.2
Scantron_Error 0 0.0 0.0
NA 13 6.2 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 37 17.6 19.0
Rarely 32 15.2 16.4
Sometimes 71 33.8 36.4
Often 55 26.2 28.2
Scantron_Error 0 0.0 0.0
NA 15 7.1 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 95 30.2 31.7
Rarely 95 30.2 31.7
Sometimes 80 25.4 26.7
Often 30 9.5 10.0
Scantron_Error 0 0.0 0.0
NA 15 4.8 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 19.7 21.3
Rarely 83 26.3 28.5
Sometimes 102 32.4 35.1
Often 44 14.0 15.1
Scantron_Error 0 0.0 0.0
NA 24 7.6 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 64 20.3 22.0
Rarely 51 16.2 17.5
Sometimes 92 29.2 31.6
Often 84 26.7 28.9
Scantron_Error 0 0.0 0.0
NA 24 7.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 122 34.3 35.6
Rarely 104 29.2 30.3
Sometimes 92 25.8 26.8
Often 25 7.0 7.3
Scantron_Error 0 0.0 0.0
NA 13 3.7 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 88 24.7 27.5
Rarely 94 26.4 29.4
Sometimes 109 30.6 34.1
Often 29 8.1 9.1
Scantron_Error 0 0.0 0.0
NA 36 10.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 73 20.5 23.2
Rarely 77 21.6 24.4
Sometimes 107 30.1 34.0
Often 58 16.3 18.4
Scantron_Error 0 0.0 0.0
NA 41 11.5 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 241 41.2 43.1
Rarely 123 21.0 22.0
Sometimes 152 26.0 27.2
Often 43 7.4 7.7
Scantron_Error 0 0.0 0.0
NA 26 4.4 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 166 28.4 32.4
Rarely 119 20.3 23.2
Sometimes 176 30.1 34.3
Often 52 8.9 10.1
Scantron_Error 0 0.0 0.0
NA 72 12.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 140 23.9 27.3
Rarely 77 13.2 15.0
Sometimes 188 32.1 36.6
Often 108 18.5 21.1
Scantron_Error 0 0.0 0.0
NA 72 12.3 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 661 37.7 39.1
Rarely 483 27.5 28.6
Sometimes 441 25.1 26.1
Often 103 5.9 6.1
Scantron_Error 2 0.1 0.1
NA 64 3.6 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 484 27.6 30.1
Rarely 445 25.4 27.7
Sometimes 532 30.3 33.1
Often 146 8.3 9.1
Scantron_Error 1 0.1 0.1
NA 146 8.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 398 22.7 25.0
Rarely 321 18.3 20.1
Sometimes 543 31.0 34.0
Often 330 18.8 20.7
Scantron_Error 3 0.2 0.2
NA 159 9.1 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 6 37.5 37.5
Rarely 6 37.5 37.5
Sometimes 2 12.5 12.5
Often 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 4 25.0 26.7
Rarely 6 37.5 40.0
Sometimes 2 12.5 13.3
Often 3 18.8 20.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 5 31.2 33.3
Rarely 2 12.5 13.3
Sometimes 6 37.5 40.0
Often 2 12.5 13.3
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D3J

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often

All data

# 1
  d3j1 <- as.factor(d[,"d3j1"])
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1451 40.8 42.4
Rarely 1165 32.8 34.0
Sometimes 602 16.9 17.6
Often 206 5.8 6.0
Scantron_Error 1 0.0 0.0
NA 132 3.7 NA
Total 3557 100.0 100.0
#2
  d3j2 <- as.factor(d[,"d3j2"])
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1116 31.4 34.4
Rarely 1149 32.3 35.4
Sometimes 737 20.7 22.7
Often 245 6.9 7.5
Scantron_Error 1 0.0 0.0
NA 309 8.7 NA
Total 3557 100.0 100.0
#3
  d3j3 <- as.factor(d[,"d3j3"])
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 995 28.0 30.8
Rarely 1008 28.3 31.2
Sometimes 798 22.4 24.7
Often 428 12.0 13.3
Scantron_Error 0 0.0 0.0
NA 328 9.2 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 91 28.3 28.7
Rarely 107 33.3 33.8
Sometimes 83 25.9 26.2
Often 36 11.2 11.4
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 19.3 19.9
Rarely 113 35.2 36.3
Sometimes 94 29.3 30.2
Often 42 13.1 13.5
Scantron_Error 0 0.0 0.0
NA 10 3.1 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 56 17.4 17.9
Rarely 99 30.8 31.6
Sometimes 114 35.5 36.4
Often 44 13.7 14.1
Scantron_Error 0 0.0 0.0
NA 8 2.5 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 72 34.3 35.3
Rarely 66 31.4 32.4
Sometimes 47 22.4 23.0
Often 19 9.0 9.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 58 27.6 29.9
Rarely 57 27.1 29.4
Sometimes 53 25.2 27.3
Often 26 12.4 13.4
Scantron_Error 0 0.0 0.0
NA 16 7.6 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 55 26.2 28.6
Rarely 43 20.5 22.4
Sometimes 65 31.0 33.9
Often 29 13.8 15.1
Scantron_Error 0 0.0 0.0
NA 18 8.6 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 105 33.3 35.1
Rarely 110 34.9 36.8
Sometimes 61 19.4 20.4
Often 23 7.3 7.7
Scantron_Error 0 0.0 0.0
NA 16 5.1 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 78 24.8 26.9
Rarely 106 33.7 36.6
Sometimes 71 22.5 24.5
Often 35 11.1 12.1
Scantron_Error 0 0.0 0.0
NA 25 7.9 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 73 23.2 25.3
Rarely 87 27.6 30.2
Sometimes 65 20.6 22.6
Often 63 20.0 21.9
Scantron_Error 0 0.0 0.0
NA 27 8.6 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 143 40.2 42.3
Rarely 115 32.3 34.0
Sometimes 61 17.1 18.0
Often 19 5.3 5.6
Scantron_Error 0 0.0 0.0
NA 18 5.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 108 30.3 34.0
Rarely 115 32.3 36.2
Sometimes 74 20.8 23.3
Often 21 5.9 6.6
Scantron_Error 0 0.0 0.0
NA 38 10.7 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 97 27.2 31.0
Rarely 110 30.9 35.1
Sometimes 74 20.8 23.6
Often 32 9.0 10.2
Scantron_Error 0 0.0 0.0
NA 43 12.1 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 245 41.9 43.8
Rarely 192 32.8 34.3
Sometimes 87 14.9 15.5
Often 36 6.2 6.4
Scantron_Error 0 0.0 0.0
NA 25 4.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 179 30.6 34.7
Rarely 185 31.6 35.9
Sometimes 110 18.8 21.3
Often 42 7.2 8.1
Scantron_Error 0 0.0 0.0
NA 69 11.8 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 158 27.0 30.6
Rarely 169 28.9 32.7
Sometimes 110 18.8 21.3
Often 80 13.7 15.5
Scantron_Error 0 0.0 0.0
NA 68 11.6 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 785 44.8 46.4
Rarely 569 32.4 33.6
Sometimes 263 15.0 15.6
Often 73 4.2 4.3
Scantron_Error 1 0.1 0.1
NA 63 3.6 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 622 35.5 38.8
Rarely 570 32.5 35.5
Sometimes 333 19.0 20.8
Often 78 4.4 4.9
Scantron_Error 1 0.1 0.1
NA 150 8.6 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 547 31.2 34.4
Rarely 496 28.3 31.2
Sometimes 369 21.0 23.2
Often 179 10.2 11.3
Scantron_Error 0 0.0 0.0
NA 163 9.3 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 10 62.5 62.5
Rarely 6 37.5 37.5
Sometimes 0 0.0 0.0
Often 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 9 56.2 60.0
Rarely 3 18.8 20.0
Sometimes 2 12.5 13.3
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 9 56.2 60.0
Rarely 4 25.0 26.7
Sometimes 1 6.2 6.7
Often 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

D4

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree

All data

# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 1762 49.5 50.6 49.5 50.6
Somewhat_Agree 1383 38.9 39.8 88.4 90.4
Somewhat_Disagree 269 7.6 7.7 96.0 98.1
Strongly_Disagree 57 1.6 1.6 97.6 99.8
Scantron_Error 8 0.2 0.2 97.8 100.0
NA 78 2.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 2251 63.3 64.5 63.3 64.5
Somewhat_Agree 1092 30.7 31.3 94.0 95.8
Somewhat_Disagree 121 3.4 3.5 97.4 99.2
Strongly_Disagree 19 0.5 0.5 97.9 99.8
Scantron_Error 8 0.2 0.2 98.1 100.0
NA 66 1.9 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 1659 46.6 47.9 46.6 47.9
Somewhat_Agree 1384 38.9 40.0 85.5 87.9
Somewhat_Disagree 351 9.9 10.1 95.4 98.0
Strongly_Disagree 59 1.7 1.7 97.1 99.7
Scantron_Error 9 0.3 0.3 97.3 100.0
NA 95 2.7 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 1695 47.7 48.9 47.7 48.9
Somewhat_Agree 1421 39.9 41.0 87.6 89.9
Somewhat_Disagree 297 8.3 8.6 96.0 98.5
Strongly_Disagree 47 1.3 1.4 97.3 99.9
Scantron_Error 5 0.1 0.1 97.4 100.0
NA 92 2.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 1489 41.9 42.8 41.9 42.8
Somewhat_Agree 1373 38.6 39.5 80.5 82.3
Somewhat_Disagree 506 14.2 14.5 94.7 96.8
Strongly_Disagree 105 3.0 3.0 97.6 99.9
Scantron_Error 5 0.1 0.1 97.8 100.0
NA 79 2.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 2258 63.5 64.8 63.5 64.8
Somewhat_Agree 1126 31.7 32.3 95.1 97.1
Somewhat_Disagree 72 2.0 2.1 97.2 99.2
Strongly_Disagree 22 0.6 0.6 97.8 99.8
Scantron_Error 7 0.2 0.2 98.0 100.0
NA 72 2.0 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 1262 35.5 36.4 35.5 36.4
Somewhat_Agree 1605 45.1 46.3 80.6 82.7
Somewhat_Disagree 458 12.9 13.2 93.5 96.0
Strongly_Disagree 137 3.9 4.0 97.3 99.9
Scantron_Error 3 0.1 0.1 97.4 100.0
NA 92 2.6 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 2179 61.3 62.7 61.3 62.7
Somewhat_Agree 1110 31.2 31.9 92.5 94.6
Somewhat_Disagree 156 4.4 4.5 96.9 99.1
Strongly_Disagree 27 0.8 0.8 97.6 99.9
Scantron_Error 4 0.1 0.1 97.7 100.0
NA 81 2.3 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 1998 56.2 57.4 56.2 57.4
Somewhat_Agree 1231 34.6 35.3 90.8 92.7
Somewhat_Disagree 215 6.0 6.2 96.8 98.9
Strongly_Disagree 31 0.9 0.9 97.7 99.8
Scantron_Error 8 0.2 0.2 97.9 100.0
NA 74 2.1 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 1202 33.8 34.6 33.8 34.6
Somewhat_Agree 1396 39.2 40.1 73.0 74.7
Somewhat_Disagree 734 20.6 21.1 93.7 95.8
Strongly_Disagree 137 3.9 3.9 97.5 99.7
Scantron_Error 10 0.3 0.3 97.8 100.0
NA 78 2.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 1856 52.2 53.3 52.2 53.3
Somewhat_Agree 1309 36.8 37.6 89.0 90.8
Somewhat_Disagree 251 7.1 7.2 96.0 98.0
Strongly_Disagree 63 1.8 1.8 97.8 99.8
Scantron_Error 6 0.2 0.2 98.0 100.0
NA 72 2.0 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 2357 66.3 67.7 66.3 67.7
Somewhat_Agree 887 24.9 25.5 91.2 93.2
Somewhat_Disagree 183 5.1 5.3 96.3 98.4
Strongly_Disagree 52 1.5 1.5 97.8 99.9
Scantron_Error 2 0.1 0.1 97.9 100.0
NA 76 2.1 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 151 47.0 47.5 47.0 47.5
Somewhat_Agree 136 42.4 42.8 89.4 90.3
Somewhat_Disagree 25 7.8 7.9 97.2 98.1
Strongly_Disagree 6 1.9 1.9 99.1 100.0
Scantron_Error 0 0.0 0.0 99.1 100.0
NA 3 0.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 193 60.1 60.3 60.1 60.3
Somewhat_Agree 109 34.0 34.1 94.1 94.4
Somewhat_Disagree 16 5.0 5.0 99.1 99.4
Strongly_Disagree 2 0.6 0.6 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 140 43.6 43.8 43.6 43.8
Somewhat_Agree 140 43.6 43.8 87.2 87.5
Somewhat_Disagree 35 10.9 10.9 98.1 98.4
Strongly_Disagree 4 1.2 1.2 99.4 99.7
Scantron_Error 1 0.3 0.3 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 145 45.2 45.5 45.2 45.5
Somewhat_Agree 142 44.2 44.5 89.4 90.0
Somewhat_Disagree 30 9.3 9.4 98.8 99.4
Strongly_Disagree 2 0.6 0.6 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 140 43.6 43.8 43.6 43.8
Somewhat_Agree 125 38.9 39.1 82.6 82.8
Somewhat_Disagree 47 14.6 14.7 97.2 97.5
Strongly_Disagree 8 2.5 2.5 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 201 62.6 63.2 62.6 63.2
Somewhat_Agree 110 34.3 34.6 96.9 97.8
Somewhat_Disagree 5 1.6 1.6 98.4 99.4
Strongly_Disagree 1 0.3 0.3 98.8 99.7
Scantron_Error 1 0.3 0.3 99.1 100.0
NA 3 0.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 101 31.5 31.7 31.5 31.7
Somewhat_Agree 156 48.6 48.9 80.1 80.6
Somewhat_Disagree 49 15.3 15.4 95.3 95.9
Strongly_Disagree 13 4.0 4.1 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 202 62.9 63.1 62.9 63.1
Somewhat_Agree 103 32.1 32.2 95.0 95.3
Somewhat_Disagree 13 4.0 4.1 99.1 99.4
Strongly_Disagree 2 0.6 0.6 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 171 53.3 53.6 53.3 53.6
Somewhat_Agree 126 39.3 39.5 92.5 93.1
Somewhat_Disagree 21 6.5 6.6 99.1 99.7
Strongly_Disagree 1 0.3 0.3 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 100 31.2 31.2 31.2 31.2
Somewhat_Agree 136 42.4 42.5 73.5 73.8
Somewhat_Disagree 75 23.4 23.4 96.9 97.2
Strongly_Disagree 9 2.8 2.8 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 155 48.3 48.4 48.3 48.4
Somewhat_Agree 131 40.8 40.9 89.1 89.4
Somewhat_Disagree 30 9.3 9.4 98.4 98.8
Strongly_Disagree 4 1.2 1.2 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 213 66.4 66.6 66.4 66.6
Somewhat_Agree 82 25.5 25.6 91.9 92.2
Somewhat_Disagree 21 6.5 6.6 98.4 98.8
Strongly_Disagree 4 1.2 1.2 99.7 100.0
Scantron_Error 0 0.0 0.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0

Northern CA

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 97 46.2 46.6 46.2 46.6
Somewhat_Agree 87 41.4 41.8 87.6 88.5
Somewhat_Disagree 19 9.0 9.1 96.7 97.6
Strongly_Disagree 5 2.4 2.4 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 123 58.6 59.1 58.6 59.1
Somewhat_Agree 77 36.7 37.0 95.2 96.2
Somewhat_Disagree 7 3.3 3.4 98.6 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 102 48.6 48.8 48.6 48.8
Somewhat_Agree 86 41.0 41.1 89.5 90.0
Somewhat_Disagree 19 9.0 9.1 98.6 99.0
Strongly_Disagree 2 1.0 1.0 99.5 100.0
Scantron_Error 0 0.0 0.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 89 42.4 42.8 42.4 42.8
Somewhat_Agree 104 49.5 50.0 91.9 92.8
Somewhat_Disagree 14 6.7 6.7 98.6 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 80 38.1 38.3 38.1 38.3
Somewhat_Agree 88 41.9 42.1 80.0 80.4
Somewhat_Disagree 34 16.2 16.3 96.2 96.7
Strongly_Disagree 7 3.3 3.3 99.5 100.0
Scantron_Error 0 0.0 0.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 135 64.3 64.9 64.3 64.9
Somewhat_Agree 66 31.4 31.7 95.7 96.6
Somewhat_Disagree 4 1.9 1.9 97.6 98.6
Strongly_Disagree 2 1.0 1.0 98.6 99.5
Scantron_Error 1 0.5 0.5 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 63 30.0 30.6 30.0 30.6
Somewhat_Agree 105 50.0 51.0 80.0 81.6
Somewhat_Disagree 30 14.3 14.6 94.3 96.1
Strongly_Disagree 8 3.8 3.9 98.1 100.0
Scantron_Error 0 0.0 0.0 98.1 100.0
NA 4 1.9 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 119 56.7 57.2 56.7 57.2
Somewhat_Agree 75 35.7 36.1 92.4 93.3
Somewhat_Disagree 12 5.7 5.8 98.1 99.0
Strongly_Disagree 2 1.0 1.0 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 114 54.3 54.5 54.3 54.5
Somewhat_Agree 81 38.6 38.8 92.9 93.3
Somewhat_Disagree 11 5.2 5.3 98.1 98.6
Strongly_Disagree 3 1.4 1.4 99.5 100.0
Scantron_Error 0 0.0 0.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 63 30.0 30.1 30.0 30.1
Somewhat_Agree 87 41.4 41.6 71.4 71.8
Somewhat_Disagree 52 24.8 24.9 96.2 96.7
Strongly_Disagree 7 3.3 3.3 99.5 100.0
Scantron_Error 0 0.0 0.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 93 44.3 44.7 44.3 44.7
Somewhat_Agree 92 43.8 44.2 88.1 88.9
Somewhat_Disagree 22 10.5 10.6 98.6 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 131 62.4 63.0 62.4 63.0
Somewhat_Agree 63 30.0 30.3 92.4 93.3
Somewhat_Disagree 12 5.7 5.8 98.1 99.0
Strongly_Disagree 2 1.0 1.0 99.0 100.0
Scantron_Error 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0

Greater CA

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 150 47.6 49.5 47.6 49.5
Somewhat_Agree 113 35.9 37.3 83.5 86.8
Somewhat_Disagree 34 10.8 11.2 94.3 98.0
Strongly_Disagree 6 1.9 2.0 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 189 60.0 62.2 60.0 62.2
Somewhat_Agree 92 29.2 30.3 89.2 92.4
Somewhat_Disagree 19 6.0 6.2 95.2 98.7
Strongly_Disagree 3 1.0 1.0 96.2 99.7
Scantron_Error 1 0.3 0.3 96.5 100.0
NA 11 3.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 141 44.8 47.2 44.8 47.2
Somewhat_Agree 125 39.7 41.8 84.4 89.0
Somewhat_Disagree 27 8.6 9.0 93.0 98.0
Strongly_Disagree 6 1.9 2.0 94.9 100.0
Scantron_Error 0 0.0 0.0 94.9 100.0
NA 16 5.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 141 44.8 46.7 44.8 46.7
Somewhat_Agree 133 42.2 44.0 87.0 90.7
Somewhat_Disagree 23 7.3 7.6 94.3 98.3
Strongly_Disagree 5 1.6 1.7 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 115 36.5 38.1 36.5 38.1
Somewhat_Agree 128 40.6 42.4 77.1 80.5
Somewhat_Disagree 49 15.6 16.2 92.7 96.7
Strongly_Disagree 10 3.2 3.3 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 191 60.6 63.0 60.6 63.0
Somewhat_Agree 107 34.0 35.3 94.6 98.3
Somewhat_Disagree 5 1.6 1.7 96.2 100.0
Strongly_Disagree 0 0.0 0.0 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 116 36.8 38.4 36.8 38.4
Somewhat_Agree 133 42.2 44.0 79.0 82.5
Somewhat_Disagree 47 14.9 15.6 94.0 98.0
Strongly_Disagree 6 1.9 2.0 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 179 56.8 59.1 56.8 59.1
Somewhat_Agree 106 33.7 35.0 90.5 94.1
Somewhat_Disagree 15 4.8 5.0 95.2 99.0
Strongly_Disagree 3 1.0 1.0 96.2 100.0
Scantron_Error 0 0.0 0.0 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 174 55.2 57.6 55.2 57.6
Somewhat_Agree 105 33.3 34.8 88.6 92.4
Somewhat_Disagree 22 7.0 7.3 95.6 99.7
Strongly_Disagree 1 0.3 0.3 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 90 28.6 29.7 28.6 29.7
Somewhat_Agree 121 38.4 39.9 67.0 69.6
Somewhat_Disagree 76 24.1 25.1 91.1 94.7
Strongly_Disagree 15 4.8 5.0 95.9 99.7
Scantron_Error 1 0.3 0.3 96.2 100.0
NA 12 3.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 155 49.2 51.3 49.2 51.3
Somewhat_Agree 117 37.1 38.7 86.3 90.1
Somewhat_Disagree 27 8.6 8.9 94.9 99.0
Strongly_Disagree 3 1.0 1.0 95.9 100.0
Scantron_Error 0 0.0 0.0 95.9 100.0
NA 13 4.1 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 199 63.2 65.5 63.2 65.5
Somewhat_Agree 81 25.7 26.6 88.9 92.1
Somewhat_Disagree 19 6.0 6.2 94.9 98.4
Strongly_Disagree 5 1.6 1.6 96.5 100.0
Scantron_Error 0 0.0 0.0 96.5 100.0
NA 11 3.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0

Detroit

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 168 47.2 47.6 47.2 47.6
Somewhat_Agree 143 40.2 40.5 87.4 88.1
Somewhat_Disagree 33 9.3 9.3 96.6 97.5
Strongly_Disagree 8 2.2 2.3 98.9 99.7
Scantron_Error 1 0.3 0.3 99.2 100.0
NA 3 0.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 221 62.1 62.4 62.1 62.4
Somewhat_Agree 120 33.7 33.9 95.8 96.3
Somewhat_Disagree 11 3.1 3.1 98.9 99.4
Strongly_Disagree 1 0.3 0.3 99.2 99.7
Scantron_Error 1 0.3 0.3 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 155 43.5 44.5 43.5 44.5
Somewhat_Agree 145 40.7 41.7 84.3 86.2
Somewhat_Disagree 39 11.0 11.2 95.2 97.4
Strongly_Disagree 8 2.2 2.3 97.5 99.7
Scantron_Error 1 0.3 0.3 97.8 100.0
NA 8 2.2 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 144 40.4 41.1 40.4 41.1
Somewhat_Agree 154 43.3 44.0 83.7 85.1
Somewhat_Disagree 44 12.4 12.6 96.1 97.7
Strongly_Disagree 7 2.0 2.0 98.0 99.7
Scantron_Error 1 0.3 0.3 98.3 100.0
NA 6 1.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 146 41.0 41.7 41.0 41.7
Somewhat_Agree 140 39.3 40.0 80.3 81.7
Somewhat_Disagree 52 14.6 14.9 94.9 96.6
Strongly_Disagree 12 3.4 3.4 98.3 100.0
Scantron_Error 0 0.0 0.0 98.3 100.0
NA 6 1.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 207 58.1 58.5 58.1 58.5
Somewhat_Agree 136 38.2 38.4 96.3 96.9
Somewhat_Disagree 11 3.1 3.1 99.4 100.0
Strongly_Disagree 0 0.0 0.0 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 137 38.5 38.9 38.5 38.9
Somewhat_Agree 154 43.3 43.8 81.7 82.7
Somewhat_Disagree 48 13.5 13.6 95.2 96.3
Strongly_Disagree 13 3.7 3.7 98.9 100.0
Scantron_Error 0 0.0 0.0 98.9 100.0
NA 4 1.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 225 63.2 63.6 63.2 63.6
Somewhat_Agree 108 30.3 30.5 93.5 94.1
Somewhat_Disagree 18 5.1 5.1 98.6 99.2
Strongly_Disagree 3 0.8 0.8 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 189 53.1 53.5 53.1 53.5
Somewhat_Agree 136 38.2 38.5 91.3 92.1
Somewhat_Disagree 20 5.6 5.7 96.9 97.7
Strongly_Disagree 7 2.0 2.0 98.9 99.7
Scantron_Error 1 0.3 0.3 99.2 100.0
NA 3 0.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 121 34.0 34.5 34.0 34.5
Somewhat_Agree 143 40.2 40.7 74.2 75.2
Somewhat_Disagree 71 19.9 20.2 94.1 95.4
Strongly_Disagree 15 4.2 4.3 98.3 99.7
Scantron_Error 1 0.3 0.3 98.6 100.0
NA 5 1.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 187 52.5 52.8 52.5 52.8
Somewhat_Agree 133 37.4 37.6 89.9 90.4
Somewhat_Disagree 25 7.0 7.1 96.9 97.5
Strongly_Disagree 8 2.2 2.3 99.2 99.7
Scantron_Error 1 0.3 0.3 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 215 60.4 60.7 60.4 60.7
Somewhat_Agree 106 29.8 29.9 90.2 90.7
Somewhat_Disagree 23 6.5 6.5 96.6 97.2
Strongly_Disagree 10 2.8 2.8 99.4 100.0
Scantron_Error 0 0.0 0.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0

Louisiana

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 293 50.1 51.6 50.1 51.6
Somewhat_Agree 219 37.4 38.6 87.5 90.1
Somewhat_Disagree 46 7.9 8.1 95.4 98.2
Strongly_Disagree 10 1.7 1.8 97.1 100.0
Scantron_Error 0 0.0 0.0 97.1 100.0
NA 17 2.9 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 379 64.8 65.9 64.8 65.9
Somewhat_Agree 171 29.2 29.7 94.0 95.7
Somewhat_Disagree 19 3.2 3.3 97.3 99.0
Strongly_Disagree 6 1.0 1.0 98.3 100.0
Scantron_Error 0 0.0 0.0 98.3 100.0
NA 10 1.7 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 288 49.2 50.4 49.2 50.4
Somewhat_Agree 213 36.4 37.3 85.6 87.7
Somewhat_Disagree 56 9.6 9.8 95.2 97.5
Strongly_Disagree 13 2.2 2.3 97.4 99.8
Scantron_Error 1 0.2 0.2 97.6 100.0
NA 14 2.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 302 51.6 53.4 51.6 53.4
Somewhat_Agree 210 35.9 37.1 87.5 90.5
Somewhat_Disagree 44 7.5 7.8 95.0 98.2
Strongly_Disagree 10 1.7 1.8 96.8 100.0
Scantron_Error 0 0.0 0.0 96.8 100.0
NA 19 3.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 273 46.7 47.6 46.7 47.6
Somewhat_Agree 201 34.4 35.1 81.0 82.7
Somewhat_Disagree 82 14.0 14.3 95.0 97.0
Strongly_Disagree 16 2.7 2.8 97.8 99.8
Scantron_Error 1 0.2 0.2 97.9 100.0
NA 12 2.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 377 64.4 65.9 64.4 65.9
Somewhat_Agree 177 30.3 30.9 94.7 96.9
Somewhat_Disagree 10 1.7 1.7 96.4 98.6
Strongly_Disagree 6 1.0 1.0 97.4 99.7
Scantron_Error 2 0.3 0.3 97.8 100.0
NA 13 2.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 232 39.7 40.8 39.7 40.8
Somewhat_Agree 238 40.7 41.8 80.3 82.6
Somewhat_Disagree 67 11.5 11.8 91.8 94.4
Strongly_Disagree 32 5.5 5.6 97.3 100.0
Scantron_Error 0 0.0 0.0 97.3 100.0
NA 16 2.7 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 368 62.9 64.7 62.9 64.7
Somewhat_Agree 161 27.5 28.3 90.4 93.0
Somewhat_Disagree 32 5.5 5.6 95.9 98.6
Strongly_Disagree 8 1.4 1.4 97.3 100.0
Scantron_Error 0 0.0 0.0 97.3 100.0
NA 16 2.7 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 352 60.2 61.4 60.2 61.4
Somewhat_Agree 178 30.4 31.1 90.6 92.5
Somewhat_Disagree 32 5.5 5.6 96.1 98.1
Strongly_Disagree 9 1.5 1.6 97.6 99.7
Scantron_Error 2 0.3 0.3 97.9 100.0
NA 12 2.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 223 38.1 39.1 38.1 39.1
Somewhat_Agree 222 37.9 38.9 76.1 77.9
Somewhat_Disagree 101 17.3 17.7 93.3 95.6
Strongly_Disagree 23 3.9 4.0 97.3 99.6
Scantron_Error 2 0.3 0.4 97.6 100.0
NA 14 2.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 334 57.1 58.3 57.1 58.3
Somewhat_Agree 183 31.3 31.9 88.4 90.2
Somewhat_Disagree 41 7.0 7.2 95.4 97.4
Strongly_Disagree 15 2.6 2.6 97.9 100.0
Scantron_Error 0 0.0 0.0 97.9 100.0
NA 12 2.1 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 406 69.4 71.4 69.4 71.4
Somewhat_Agree 122 20.9 21.4 90.3 92.8
Somewhat_Disagree 27 4.6 4.7 94.9 97.5
Strongly_Disagree 14 2.4 2.5 97.3 100.0
Scantron_Error 0 0.0 0.0 97.3 100.0
NA 16 2.7 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0

Georgia

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 894 51.0 52.2 51.0 52.2
Somewhat_Agree 679 38.7 39.6 89.7 91.8
Somewhat_Disagree 112 6.4 6.5 96.1 98.3
Strongly_Disagree 22 1.3 1.3 97.3 99.6
Scantron_Error 7 0.4 0.4 97.7 100.0
NA 40 2.3 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 1138 64.9 66.4 64.9 66.4
Somewhat_Agree 515 29.4 30.0 94.2 96.4
Somewhat_Disagree 49 2.8 2.9 97.0 99.3
Strongly_Disagree 6 0.3 0.4 97.4 99.6
Scantron_Error 6 0.3 0.4 97.7 100.0
NA 40 2.3 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 828 47.2 48.7 47.2 48.7
Somewhat_Agree 670 38.2 39.4 85.4 88.1
Somewhat_Disagree 170 9.7 10.0 95.1 98.1
Strongly_Disagree 26 1.5 1.5 96.6 99.6
Scantron_Error 6 0.3 0.4 96.9 100.0
NA 54 3.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 868 49.5 50.9 49.5 50.9
Somewhat_Agree 671 38.3 39.4 87.7 90.3
Somewhat_Disagree 140 8.0 8.2 95.7 98.5
Strongly_Disagree 21 1.2 1.2 96.9 99.8
Scantron_Error 4 0.2 0.2 97.1 100.0
NA 50 2.9 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 729 41.6 42.7 41.6 42.7
Somewhat_Agree 684 39.0 40.0 80.6 82.7
Somewhat_Disagree 239 13.6 14.0 94.2 96.7
Strongly_Disagree 52 3.0 3.0 97.1 99.8
Scantron_Error 4 0.2 0.2 97.4 100.0
NA 46 2.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 1139 64.9 66.5 64.9 66.5
Somewhat_Agree 523 29.8 30.5 94.8 97.0
Somewhat_Disagree 36 2.1 2.1 96.8 99.1
Strongly_Disagree 13 0.7 0.8 97.5 99.8
Scantron_Error 3 0.2 0.2 97.7 100.0
NA 40 2.3 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 604 34.4 35.5 34.4 35.5
Somewhat_Agree 813 46.4 47.8 80.8 83.3
Somewhat_Disagree 216 12.3 12.7 93.1 96.0
Strongly_Disagree 65 3.7 3.8 96.8 99.8
Scantron_Error 3 0.2 0.2 97.0 100.0
NA 53 3.0 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 1076 61.3 63.1 61.3 63.1
Somewhat_Agree 551 31.4 32.3 92.8 95.4
Somewhat_Disagree 66 3.8 3.9 96.5 99.2
Strongly_Disagree 9 0.5 0.5 97.0 99.8
Scantron_Error 4 0.2 0.2 97.3 100.0
NA 48 2.7 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 990 56.4 57.9 56.4 57.9
Somewhat_Agree 599 34.2 35.0 90.6 92.9
Somewhat_Disagree 107 6.1 6.3 96.7 99.1
Strongly_Disagree 10 0.6 0.6 97.3 99.7
Scantron_Error 5 0.3 0.3 97.5 100.0
NA 43 2.5 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 597 34.0 34.9 34.0 34.9
Somewhat_Agree 686 39.1 40.1 73.1 75.1
Somewhat_Disagree 352 20.1 20.6 93.2 95.7
Strongly_Disagree 68 3.9 4.0 97.1 99.6
Scantron_Error 6 0.3 0.4 97.4 100.0
NA 45 2.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 925 52.7 54.0 52.7 54.0
Somewhat_Agree 645 36.8 37.7 89.5 91.7
Somewhat_Disagree 106 6.0 6.2 95.6 97.9
Strongly_Disagree 31 1.8 1.8 97.3 99.7
Scantron_Error 5 0.3 0.3 97.6 100.0
NA 42 2.4 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 1185 67.6 69.3 67.6 69.3
Somewhat_Agree 428 24.4 25.0 92.0 94.3
Somewhat_Disagree 78 4.4 4.6 96.4 98.9
Strongly_Disagree 17 1.0 1.0 97.4 99.9
Scantron_Error 2 0.1 0.1 97.5 100.0
NA 44 2.5 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0

Michigan

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 9 56.2 60 56.2 60
Somewhat_Agree 6 37.5 40 93.8 100
Somewhat_Disagree 0 0.0 0 93.8 100
Strongly_Disagree 0 0.0 0 93.8 100
Scantron_Error 0 0.0 0 93.8 100
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100 100.0 100
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 8 50 50 50 50
Somewhat_Agree 8 50 50 100 100
Somewhat_Disagree 0 0 0 100 100
Strongly_Disagree 0 0 0 100 100
Scantron_Error 0 0 0 100 100
Total 16 100 100 100 100
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 5 31.2 33.3 31.2 33.3
Somewhat_Agree 5 31.2 33.3 62.5 66.7
Somewhat_Disagree 5 31.2 33.3 93.8 100.0
Strongly_Disagree 0 0.0 0.0 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 6 37.5 37.5 37.5 37.5
Somewhat_Agree 7 43.8 43.8 81.2 81.2
Somewhat_Disagree 2 12.5 12.5 93.8 93.8
Strongly_Disagree 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 6 37.5 37.5 37.5 37.5
Somewhat_Agree 7 43.8 43.8 81.2 81.2
Somewhat_Disagree 3 18.8 18.8 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 8 50.0 50.0 50.0 50.0
Somewhat_Agree 7 43.8 43.8 93.8 93.8
Somewhat_Disagree 1 6.2 6.2 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 9 56.2 56.2 56.2 56.2
Somewhat_Agree 6 37.5 37.5 93.8 93.8
Somewhat_Disagree 1 6.2 6.2 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 10 62.5 62.5 62.5 62.5
Somewhat_Agree 6 37.5 37.5 100.0 100.0
Somewhat_Disagree 0 0.0 0.0 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 8 50.0 50.0 50.0 50.0
Somewhat_Agree 6 37.5 37.5 87.5 87.5
Somewhat_Disagree 2 12.5 12.5 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 8 50.0 50.0 50.0 50.0
Somewhat_Agree 1 6.2 6.2 56.2 56.2
Somewhat_Disagree 7 43.8 43.8 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 7 43.8 43.8 43.8 43.8
Somewhat_Agree 8 50.0 50.0 93.8 93.8
Somewhat_Disagree 0 0.0 0.0 93.8 93.8
Strongly_Disagree 1 6.2 6.2 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 8 50.0 50.0 50.0 50.0
Somewhat_Agree 5 31.2 31.2 81.2 81.2
Somewhat_Disagree 3 18.8 18.8 100.0 100.0
Strongly_Disagree 0 0.0 0.0 100.0 100.0
Scantron_Error 0 0.0 0.0 100.0 100.0
Total 16 100.0 100.0 100.0 100.0

D5

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer"

All data

# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 2868 80.6 82.3
Yes 323 9.1 9.3
Dont_know_not_sure 262 7.4 7.5
Prefer_not_to_answer 31 0.9 0.9
Scantron_Error 1 0.0 0.0
NA 72 2.0 NA
Total 3557 100.0 100.0
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 2362 66.4 67.8
Yes 920 25.9 26.4
Dont_know_not_sure 148 4.2 4.2
Prefer_not_to_answer 53 1.5 1.5
Scantron_Error 1 0.0 0.0
NA 73 2.1 NA
Total 3557 100.0 100.0
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 2936 82.5 84.4
Yes 343 9.6 9.9
Dont_know_not_sure 162 4.6 4.7
Prefer_not_to_answer 37 1.0 1.1
Scantron_Error 1 0.0 0.0
NA 78 2.2 NA
Total 3557 100.0 100.0
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 3014 84.7 86.4
Yes 382 10.7 11.0
Dont_know_not_sure 52 1.5 1.5
Prefer_not_to_answer 37 1.0 1.1
Scantron_Error 2 0.1 0.1
NA 70 2.0 NA
Total 3557 100.0 100.0
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 2101 59.1 60.4
Yes 979 27.5 28.1
Not_married 286 8.0 8.2
Dont_know_not_sure 49 1.4 1.4
Prefer_not_to_answer 58 1.6 1.7
Scantron_Error 6 0.2 0.2
NA 78 2.2 NA
Total 3557 100.0 100.0
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")  
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 2152 60.5 62.2
Once 230 6.5 6.6
More_than_once 527 14.8 15.2
Dont_know_not_sure 400 11.2 11.6
Prefer_not_to_answer 148 4.2 4.3
Scantron_Error 3 0.1 0.1
NA 97 2.7 NA
Total 3557 100.0 100.0
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?") 
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 2560 72.0 74.0
Once 135 3.8 3.9
More_than_once 510 14.3 14.7
Dont_know_not_sure 137 3.9 4.0
Prefer_not_to_answer 116 3.3 3.4
Scantron_Error 2 0.1 0.1
NA 97 2.7 NA
Total 3557 100.0 100.0
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 2077 58.4 60.0
Once 158 4.4 4.6
More_than_once 864 24.3 25.0
Dont_know_not_sure 252 7.1 7.3
Prefer_not_to_answer 110 3.1 3.2
Scantron_Error 1 0.0 0.0
NA 95 2.7 NA
Total 3557 100.0 100.0
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 3121 87.7 89.7
Once 150 4.2 4.3
More_than_once 113 3.2 3.2
Dont_know_not_sure 52 1.5 1.5
Prefer_not_to_answer 43 1.2 1.2
Scantron_Error 1 0.0 0.0
NA 77 2.2 NA
Total 3557 100.0 100.0
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 3181 89.4 91.4
Once 121 3.4 3.5
More_than_once 92 2.6 2.6
Dont_know_not_sure 39 1.1 1.1
Prefer_not_to_answer 45 1.3 1.3
Scantron_Error 1 0.0 0.0
NA 78 2.2 NA
Total 3557 100.0 100.0
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 3302 92.8 94.9
Once 56 1.6 1.6
More_than_once 53 1.5 1.5
Dont_know_not_sure 30 0.8 0.9
Prefer_not_to_answer 40 1.1 1.1
Scantron_Error 0 0.0 0.0
NA 76 2.1 NA
Total 3557 100.0 100.0

LA County

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 266 82.9 83.4
Yes 26 8.1 8.2
Dont_know_not_sure 25 7.8 7.8
Prefer_not_to_answer 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 218 67.9 68.3
Yes 89 27.7 27.9
Dont_know_not_sure 9 2.8 2.8
Prefer_not_to_answer 3 0.9 0.9
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 263 81.9 82.4
Yes 43 13.4 13.5
Dont_know_not_sure 9 2.8 2.8
Prefer_not_to_answer 4 1.2 1.3
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 265 82.6 83.1
Yes 46 14.3 14.4
Dont_know_not_sure 4 1.2 1.3
Prefer_not_to_answer 4 1.2 1.3
Scantron_Error 0 0.0 0.0
NA 2 0.6 NA
Total 321 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 163 50.8 51.6
Yes 130 40.5 41.1
Not_married 9 2.8 2.8
Dont_know_not_sure 10 3.1 3.2
Prefer_not_to_answer 4 1.2 1.3
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 176 54.8 55.7
Once 26 8.1 8.2
More_than_once 65 20.2 20.6
Dont_know_not_sure 35 10.9 11.1
Prefer_not_to_answer 13 4.0 4.1
Scantron_Error 1 0.3 0.3
NA 5 1.6 NA
Total 321 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 229 71.3 72.5
Once 13 4.0 4.1
More_than_once 53 16.5 16.8
Dont_know_not_sure 10 3.1 3.2
Prefer_not_to_answer 11 3.4 3.5
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 181 56.4 58.2
Once 16 5.0 5.1
More_than_once 89 27.7 28.6
Dont_know_not_sure 18 5.6 5.8
Prefer_not_to_answer 6 1.9 1.9
Scantron_Error 1 0.3 0.3
NA 10 3.1 NA
Total 321 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 274 85.4 86.4
Once 18 5.6 5.7
More_than_once 12 3.7 3.8
Dont_know_not_sure 8 2.5 2.5
Prefer_not_to_answer 5 1.6 1.6
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 284 88.5 89.9
Once 15 4.7 4.7
More_than_once 8 2.5 2.5
Dont_know_not_sure 4 1.2 1.3
Prefer_not_to_answer 5 1.6 1.6
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 296 92.2 93.7
Once 10 3.1 3.2
More_than_once 4 1.2 1.3
Dont_know_not_sure 3 0.9 0.9
Prefer_not_to_answer 3 0.9 0.9
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0

Northern CA

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 170 81.0 81.0
Yes 21 10.0 10.0
Dont_know_not_sure 17 8.1 8.1
Prefer_not_to_answer 2 1.0 1.0
Scantron_Error 0 0.0 0.0
Total 210 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 140 66.7 67.0
Yes 55 26.2 26.3
Dont_know_not_sure 9 4.3 4.3
Prefer_not_to_answer 5 2.4 2.4
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 166 79.0 79.4
Yes 32 15.2 15.3
Dont_know_not_sure 8 3.8 3.8
Prefer_not_to_answer 3 1.4 1.4
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 176 83.8 84.2
Yes 28 13.3 13.4
Dont_know_not_sure 4 1.9 1.9
Prefer_not_to_answer 1 0.5 0.5
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 109 51.9 52.2
Yes 75 35.7 35.9
Not_married 20 9.5 9.6
Dont_know_not_sure 1 0.5 0.5
Prefer_not_to_answer 4 1.9 1.9
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 128 61.0 61.2
Once 18 8.6 8.6
More_than_once 33 15.7 15.8
Dont_know_not_sure 19 9.0 9.1
Prefer_not_to_answer 11 5.2 5.3
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 152 72.4 73.1
Once 9 4.3 4.3
More_than_once 35 16.7 16.8
Dont_know_not_sure 3 1.4 1.4
Prefer_not_to_answer 9 4.3 4.3
Scantron_Error 0 0.0 0.0
NA 2 1.0 NA
Total 210 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 113 53.8 54.1
Once 14 6.7 6.7
More_than_once 60 28.6 28.7
Dont_know_not_sure 12 5.7 5.7
Prefer_not_to_answer 10 4.8 4.8
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 183 87.1 87.6
Once 13 6.2 6.2
More_than_once 8 3.8 3.8
Dont_know_not_sure 4 1.9 1.9
Prefer_not_to_answer 1 0.5 0.5
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 189 90.0 90.9
Once 8 3.8 3.8
More_than_once 8 3.8 3.8
Dont_know_not_sure 1 0.5 0.5
Prefer_not_to_answer 1 0.5 0.5
Scantron_Error 1 0.5 0.5
NA 2 1.0 NA
Total 210 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 197 93.8 94.3
Once 3 1.4 1.4
More_than_once 3 1.4 1.4
Dont_know_not_sure 4 1.9 1.9
Prefer_not_to_answer 2 1.0 1.0
Scantron_Error 0 0.0 0.0
NA 1 0.5 NA
Total 210 100.0 100.0

Greater CA

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 241 76.5 79.8
Yes 33 10.5 10.9
Dont_know_not_sure 25 7.9 8.3
Prefer_not_to_answer 2 0.6 0.7
Scantron_Error 1 0.3 0.3
NA 13 4.1 NA
Total 315 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 199 63.2 65.9
Yes 86 27.3 28.5
Dont_know_not_sure 16 5.1 5.3
Prefer_not_to_answer 1 0.3 0.3
Scantron_Error 0 0.0 0.0
NA 13 4.1 NA
Total 315 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 251 79.7 82.8
Yes 40 12.7 13.2
Dont_know_not_sure 11 3.5 3.6
Prefer_not_to_answer 1 0.3 0.3
Scantron_Error 0 0.0 0.0
NA 12 3.8 NA
Total 315 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 259 82.2 85.5
Yes 38 12.1 12.5
Dont_know_not_sure 3 1.0 1.0
Prefer_not_to_answer 2 0.6 0.7
Scantron_Error 1 0.3 0.3
NA 12 3.8 NA
Total 315 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 168 53.3 55.4
Yes 104 33.0 34.3
Not_married 21 6.7 6.9
Dont_know_not_sure 3 1.0 1.0
Prefer_not_to_answer 6 1.9 2.0
Scantron_Error 1 0.3 0.3
NA 12 3.8 NA
Total 315 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 170 54.0 56.1
Once 28 8.9 9.2
More_than_once 64 20.3 21.1
Dont_know_not_sure 29 9.2 9.6
Prefer_not_to_answer 11 3.5 3.6
Scantron_Error 1 0.3 0.3
NA 12 3.8 NA
Total 315 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 207 65.7 68.3
Once 13 4.1 4.3
More_than_once 65 20.6 21.5
Dont_know_not_sure 11 3.5 3.6
Prefer_not_to_answer 6 1.9 2.0
Scantron_Error 1 0.3 0.3
NA 12 3.8 NA
Total 315 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 153 48.6 50.3
Once 21 6.7 6.9
More_than_once 97 30.8 31.9
Dont_know_not_sure 24 7.6 7.9
Prefer_not_to_answer 9 2.9 3.0
Scantron_Error 0 0.0 0.0
NA 11 3.5 NA
Total 315 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 269 85.4 88.5
Once 15 4.8 4.9
More_than_once 12 3.8 3.9
Dont_know_not_sure 5 1.6 1.6
Prefer_not_to_answer 3 1.0 1.0
Scantron_Error 0 0.0 0.0
NA 11 3.5 NA
Total 315 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 280 88.9 91.5
Once 11 3.5 3.6
More_than_once 10 3.2 3.3
Dont_know_not_sure 3 1.0 1.0
Prefer_not_to_answer 2 0.6 0.7
Scantron_Error 0 0.0 0.0
NA 9 2.9 NA
Total 315 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 294 93.3 96.1
Once 6 1.9 2.0
More_than_once 3 1.0 1.0
Dont_know_not_sure 2 0.6 0.7
Prefer_not_to_answer 1 0.3 0.3
Scantron_Error 0 0.0 0.0
NA 9 2.9 NA
Total 315 100.0 100.0

Detroit

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 268 75.3 76.6
Yes 44 12.4 12.6
Dont_know_not_sure 35 9.8 10.0
Prefer_not_to_answer 3 0.8 0.9
Scantron_Error 0 0.0 0.0
NA 6 1.7 NA
Total 356 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 206 57.9 58.9
Yes 122 34.3 34.9
Dont_know_not_sure 19 5.3 5.4
Prefer_not_to_answer 3 0.8 0.9
Scantron_Error 0 0.0 0.0
NA 6 1.7 NA
Total 356 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 269 75.6 77.3
Yes 55 15.4 15.8
Dont_know_not_sure 22 6.2 6.3
Prefer_not_to_answer 2 0.6 0.6
Scantron_Error 0 0.0 0.0
NA 8 2.2 NA
Total 356 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 295 82.9 84.3
Yes 45 12.6 12.9
Dont_know_not_sure 6 1.7 1.7
Prefer_not_to_answer 4 1.1 1.1
Scantron_Error 0 0.0 0.0
NA 6 1.7 NA
Total 356 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 204 57.3 58.8
Yes 103 28.9 29.7
Not_married 27 7.6 7.8
Dont_know_not_sure 4 1.1 1.2
Prefer_not_to_answer 8 2.2 2.3
Scantron_Error 1 0.3 0.3
NA 9 2.5 NA
Total 356 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 214 60.1 62.6
Once 20 5.6 5.8
More_than_once 42 11.8 12.3
Dont_know_not_sure 45 12.6 13.2
Prefer_not_to_answer 21 5.9 6.1
Scantron_Error 0 0.0 0.0
NA 14 3.9 NA
Total 356 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 252 70.8 72.8
Once 15 4.2 4.3
More_than_once 50 14.0 14.5
Dont_know_not_sure 15 4.2 4.3
Prefer_not_to_answer 14 3.9 4.0
Scantron_Error 0 0.0 0.0
NA 10 2.8 NA
Total 356 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 187 52.5 54.4
Once 16 4.5 4.7
More_than_once 104 29.2 30.2
Dont_know_not_sure 27 7.6 7.8
Prefer_not_to_answer 10 2.8 2.9
Scantron_Error 0 0.0 0.0
NA 12 3.4 NA
Total 356 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 305 85.7 88.4
Once 14 3.9 4.1
More_than_once 12 3.4 3.5
Dont_know_not_sure 7 2.0 2.0
Prefer_not_to_answer 7 2.0 2.0
Scantron_Error 0 0.0 0.0
NA 11 3.1 NA
Total 356 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 317 89.0 91.9
Once 7 2.0 2.0
More_than_once 11 3.1 3.2
Dont_know_not_sure 3 0.8 0.9
Prefer_not_to_answer 7 2.0 2.0
Scantron_Error 0 0.0 0.0
NA 11 3.1 NA
Total 356 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 323 90.7 93.4
Once 7 2.0 2.0
More_than_once 5 1.4 1.4
Dont_know_not_sure 4 1.1 1.2
Prefer_not_to_answer 7 2.0 2.0
Scantron_Error 0 0.0 0.0
NA 10 2.8 NA
Total 356 100.0 100.0

Louisiana

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 462 79.0 80.9
Yes 60 10.3 10.5
Dont_know_not_sure 43 7.4 7.5
Prefer_not_to_answer 6 1.0 1.1
Scantron_Error 0 0.0 0.0
NA 14 2.4 NA
Total 585 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 424 72.5 74.6
Yes 109 18.6 19.2
Dont_know_not_sure 27 4.6 4.8
Prefer_not_to_answer 7 1.2 1.2
Scantron_Error 1 0.2 0.2
NA 17 2.9 NA
Total 585 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 474 81.0 83.5
Yes 49 8.4 8.6
Dont_know_not_sure 35 6.0 6.2
Prefer_not_to_answer 9 1.5 1.6
Scantron_Error 1 0.2 0.2
NA 17 2.9 NA
Total 585 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 494 84.4 86.7
Yes 59 10.1 10.4
Dont_know_not_sure 8 1.4 1.4
Prefer_not_to_answer 8 1.4 1.4
Scantron_Error 1 0.2 0.2
NA 15 2.6 NA
Total 585 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 365 62.4 63.6
Yes 147 25.1 25.6
Not_married 48 8.2 8.4
Dont_know_not_sure 7 1.2 1.2
Prefer_not_to_answer 7 1.2 1.2
Scantron_Error 0 0.0 0.0
NA 11 1.9 NA
Total 585 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 383 65.5 67.7
Once 28 4.8 4.9
More_than_once 70 12.0 12.4
Dont_know_not_sure 66 11.3 11.7
Prefer_not_to_answer 19 3.2 3.4
Scantron_Error 0 0.0 0.0
NA 19 3.2 NA
Total 585 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 433 74.0 76.0
Once 17 2.9 3.0
More_than_once 71 12.1 12.5
Dont_know_not_sure 29 5.0 5.1
Prefer_not_to_answer 20 3.4 3.5
Scantron_Error 0 0.0 0.0
NA 15 2.6 NA
Total 585 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 388 66.3 67.8
Once 15 2.6 2.6
More_than_once 114 19.5 19.9
Dont_know_not_sure 43 7.4 7.5
Prefer_not_to_answer 12 2.1 2.1
Scantron_Error 0 0.0 0.0
NA 13 2.2 NA
Total 585 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 535 91.5 93.9
Once 11 1.9 1.9
More_than_once 14 2.4 2.5
Dont_know_not_sure 4 0.7 0.7
Prefer_not_to_answer 6 1.0 1.1
Scantron_Error 0 0.0 0.0
NA 15 2.6 NA
Total 585 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 536 91.6 93.9
Once 12 2.1 2.1
More_than_once 10 1.7 1.8
Dont_know_not_sure 5 0.9 0.9
Prefer_not_to_answer 8 1.4 1.4
Scantron_Error 0 0.0 0.0
NA 14 2.4 NA
Total 585 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 544 93.0 95.4
Once 5 0.9 0.9
More_than_once 10 1.7 1.8
Dont_know_not_sure 4 0.7 0.7
Prefer_not_to_answer 7 1.2 1.2
Scantron_Error 0 0.0 0.0
NA 15 2.6 NA
Total 585 100.0 100.0

Georgia

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 1448 82.6 84.3
Yes 137 7.8 8.0
Dont_know_not_sure 116 6.6 6.8
Prefer_not_to_answer 16 0.9 0.9
Scantron_Error 0 0.0 0.0
NA 37 2.1 NA
Total 1754 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 1165 66.4 67.7
Yes 453 25.8 26.3
Dont_know_not_sure 68 3.9 4.0
Prefer_not_to_answer 34 1.9 2.0
Scantron_Error 0 0.0 0.0
NA 34 1.9 NA
Total 1754 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 1500 85.5 87.4
Yes 121 6.9 7.1
Dont_know_not_sure 77 4.4 4.5
Prefer_not_to_answer 18 1.0 1.0
Scantron_Error 0 0.0 0.0
NA 38 2.2 NA
Total 1754 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 1511 86.1 87.8
Yes 165 9.4 9.6
Dont_know_not_sure 26 1.5 1.5
Prefer_not_to_answer 18 1.0 1.0
Scantron_Error 0 0.0 0.0
NA 34 1.9 NA
Total 1754 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 1082 61.7 63.1
Yes 418 23.8 24.4
Not_married 158 9.0 9.2
Dont_know_not_sure 24 1.4 1.4
Prefer_not_to_answer 29 1.7 1.7
Scantron_Error 4 0.2 0.2
NA 39 2.2 NA
Total 1754 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 1071 61.1 62.7
Once 108 6.2 6.3
More_than_once 251 14.3 14.7
Dont_know_not_sure 204 11.6 11.9
Prefer_not_to_answer 73 4.2 4.3
Scantron_Error 1 0.1 0.1
NA 46 2.6 NA
Total 1754 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 1279 72.9 75.2
Once 68 3.9 4.0
More_than_once 231 13.2 13.6
Dont_know_not_sure 67 3.8 3.9
Prefer_not_to_answer 55 3.1 3.2
Scantron_Error 1 0.1 0.1
NA 53 3.0 NA
Total 1754 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 1049 59.8 61.5
Once 75 4.3 4.4
More_than_once 394 22.5 23.1
Dont_know_not_sure 125 7.1 7.3
Prefer_not_to_answer 63 3.6 3.7
Scantron_Error 0 0.0 0.0
NA 48 2.7 NA
Total 1754 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 1543 88.0 89.8
Once 79 4.5 4.6
More_than_once 54 3.1 3.1
Dont_know_not_sure 21 1.2 1.2
Prefer_not_to_answer 21 1.2 1.2
Scantron_Error 1 0.1 0.1
NA 35 2.0 NA
Total 1754 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 1563 89.1 91.0
Once 68 3.9 4.0
More_than_once 43 2.5 2.5
Dont_know_not_sure 21 1.2 1.2
Prefer_not_to_answer 22 1.3 1.3
Scantron_Error 0 0.0 0.0
NA 37 2.1 NA
Total 1754 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 1635 93.2 95.2
Once 25 1.4 1.5
More_than_once 27 1.5 1.6
Dont_know_not_sure 11 0.6 0.6
Prefer_not_to_answer 20 1.1 1.2
Scantron_Error 0 0.0 0.0
NA 36 2.1 NA
Total 1754 100.0 100.0

Michigan

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 13 81.2 81.2
Yes 2 12.5 12.5
Dont_know_not_sure 1 6.2 6.2
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 10 62.5 62.5
Yes 6 37.5 37.5
Dont_know_not_sure 0 0.0 0.0
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 13 81.2 81.2
Yes 3 18.8 18.8
Dont_know_not_sure 0 0.0 0.0
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
n % val%
No 14 87.5 87.5
Yes 1 6.2 6.2
Dont_know_not_sure 1 6.2 6.2
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
e. Were your parents separated or divorced?
n % val%
No 10 62.5 66.7
Yes 2 12.5 13.3
Not_married 3 18.8 20.0
Dont_know_not_sure 0 0.0 0.0
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 10 62.5 62.5
Once 2 12.5 12.5
More_than_once 2 12.5 12.5
Dont_know_not_sure 2 12.5 12.5
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 8 50.0 50.0
Once 0 0.0 0.0
More_than_once 5 31.2 31.2
Dont_know_not_sure 2 12.5 12.5
Prefer_not_to_answer 1 6.2 6.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 6 37.5 37.5
Once 1 6.2 6.2
More_than_once 6 37.5 37.5
Dont_know_not_sure 3 18.8 18.8
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 12 75.0 75.0
Once 0 0.0 0.0
More_than_once 1 6.2 6.2
Dont_know_not_sure 3 18.8 18.8
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 12 75.0 75.0
Once 0 0.0 0.0
More_than_once 2 12.5 12.5
Dont_know_not_sure 2 12.5 12.5
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 13 81.2 81.2
Once 0 0.0 0.0
More_than_once 1 6.2 6.2
Dont_know_not_sure 2 12.5 12.5
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0

E1

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other

All data

# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1",
                     Scantron_Error="*")
  e1_1 <- ordered(e1_1, c("High_PSA_test","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 2771 77.9 100
Scantron_Error 0 0.0 0
NA 786 22.1 NA
Total 3557 100.0 100
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_2 <- ordered(e1_2, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 940 26.4 100
Scantron_Error 0 0.0 0
NA 2617 73.6 NA
Total 3557 100.0 100
#3
  e1_3 <- as.factor(d[,"e1_3"])
  levels(e1_3) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_3 <- ordered(e1_3, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 658 18.5 100
Scantron_Error 0 0.0 0
NA 2899 81.5 NA
Total 3557 100.0 100
#4
  e1_4 <- as.factor(d[,"e1_4"])
  levels(e1_4) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_4 <- ordered(e1_4, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 68 1.9 100
Scantron_Error 0 0.0 0
NA 3489 98.1 NA
Total 3557 100.0 100
#5
  e1_5 <- as.factor(d[,"e1_5"])
  levels(e1_5) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_5 <- ordered(e1_5, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 147 4.1 100
Scantron_Error 0 0.0 0
NA 3410 95.9 NA
Total 3557 100.0 100
#6

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 256 79.8 100
Scantron_Error 0 0.0 0
NA 65 20.2 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 74 23.1 100
Scantron_Error 0 0.0 0
NA 247 76.9 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 75 23.4 100
Scantron_Error 0 0.0 0
NA 246 76.6 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 5 1.6 100
Scantron_Error 0 0.0 0
NA 316 98.4 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 5 1.6 100
Scantron_Error 0 0.0 0
NA 316 98.4 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 172 81.9 100
Scantron_Error 0 0.0 0
NA 38 18.1 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 45 21.4 100
Scantron_Error 0 0.0 0
NA 165 78.6 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 40 19 100
Scantron_Error 0 0 0
NA 170 81 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 2 1 100
Scantron_Error 0 0 0
NA 208 99 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 6 2.9 100
Scantron_Error 0 0.0 0
NA 204 97.1 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 235 74.6 100
Scantron_Error 0 0.0 0
NA 80 25.4 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 84 26.7 100
Scantron_Error 0 0.0 0
NA 231 73.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 57 18.1 100
Scantron_Error 0 0.0 0
NA 258 81.9 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 7 2.2 100
Scantron_Error 0 0.0 0
NA 308 97.8 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 16 5.1 100
Scantron_Error 0 0.0 0
NA 299 94.9 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 260 73 100
Scantron_Error 0 0 0
NA 96 27 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 112 31.5 100
Scantron_Error 0 0.0 0
NA 244 68.5 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 59 16.6 100
Scantron_Error 0 0.0 0
NA 297 83.4 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 7 2 100
Scantron_Error 0 0 0
NA 349 98 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 27 7.6 100
Scantron_Error 0 0.0 0
NA 329 92.4 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 447 76.4 100
Scantron_Error 0 0.0 0
NA 138 23.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 157 26.8 100
Scantron_Error 0 0.0 0
NA 428 73.2 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 112 19.1 100
Scantron_Error 0 0.0 0
NA 473 80.9 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 14 2.4 100
Scantron_Error 0 0.0 0
NA 571 97.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 13 2.2 100
Scantron_Error 0 0.0 0
NA 572 97.8 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 1390 79.2 100
Scantron_Error 0 0.0 0
NA 364 20.8 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 463 26.4 100
Scantron_Error 0 0.0 0
NA 1291 73.6 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 311 17.7 100
Scantron_Error 0 0.0 0
NA 1443 82.3 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 31 1.8 100
Scantron_Error 0 0.0 0
NA 1723 98.2 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 79 4.5 100
Scantron_Error 0 0.0 0
NA 1675 95.5 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 11 68.8 100
Scantron_Error 0 0.0 0
NA 5 31.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 5 31.2 100
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 4 25 100
Scantron_Error 0 0 0
NA 12 75 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 2 12.5 100
Scantron_Error 0 0.0 0
NA 14 87.5 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
rm(temp.dd) 

E2

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more

All data

  e2aa <- as.factor(d[,"e2aa"])
  levels(e2aa) <- list(No="1",
                       Yes="2",
                     Scantron_Error="*")
  e2aa <- ordered(e2aa, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "biopsies negative")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 459 12.9 15.1
No 2581 72.6 84.8
Scantron_Error 3 0.1 0.1
NA 514 14.5 NA
Total 3557 100.0 100.0
  e2ab <- as.factor(d[,"e2ab"])
  levels(e2ab) <- list(One="1",
                       Two="2",
                       Three_more="3",
                     Scantron_Error="*")
  e2ab <- ordered(e2ab, c("One","Two","Three_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "biopsies negative-number")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 253 7.1 48.2 7.1 48.2
Two 148 4.2 28.2 11.3 76.4
Three_more 124 3.5 23.6 14.8 100.0
Scantron_Error 0 0.0 0.0 14.8 100.0
NA 3032 85.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  e2ba <- as.factor(d[,"e2ba"])
  levels(e2ba) <- list(No="1",
                       Yes="2",
                     Scantron_Error="*")
  e2ba <- ordered(e2ba, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "Normal PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 1555 43.7 63.8
No 864 24.3 35.5
Scantron_Error 17 0.5 0.7
NA 1121 31.5 NA
Total 3557 100.0 100.0
  e2bb <- as.factor(d[,"e2bb"])
  levels(e2bb) <- list(One="1",
                       Two="2",
                       Three_more="3",
                     Scantron_Error="*")
  e2bb <- ordered(e2bb, c("One","Two","Three_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "Normal PSA blood tests-number")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 170 4.8 23.9 4.8 23.9
Two 268 7.5 37.7 12.3 61.6
Three_more 272 7.6 38.3 20.0 99.9
Scantron_Error 1 0.0 0.1 20.0 100.0
NA 2846 80.0 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 44 13.7 14.8
No 254 79.1 85.2
Scantron_Error 0 0.0 0.0
NA 23 7.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 24 7.5 49.0 7.5 49.0
Two 11 3.4 22.4 10.9 71.4
Three_more 14 4.4 28.6 15.3 100.0
Scantron_Error 0 0.0 0.0 15.3 100.0
NA 272 84.7 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 177 55.1 71.1
No 69 21.5 27.7
Scantron_Error 3 0.9 1.2
NA 72 22.4 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 13 4.0 21.7 4.0 21.7
Two 19 5.9 31.7 10.0 53.3
Three_more 28 8.7 46.7 18.7 100.0
Scantron_Error 0 0.0 0.0 18.7 100.0
NA 261 81.3 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 30 14.3 15.9
No 159 75.7 84.1
Scantron_Error 0 0.0 0.0
NA 21 10.0 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 17 8.1 53.1 8.1 53.1
Two 9 4.3 28.1 12.4 81.2
Three_more 6 2.9 18.8 15.2 100.0
Scantron_Error 0 0.0 0.0 15.2 100.0
NA 178 84.8 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 105 50.0 70
No 45 21.4 30
Scantron_Error 0 0.0 0
NA 60 28.6 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 13 6.2 28.3 6.2 28.3
Two 20 9.5 43.5 15.7 71.7
Three_more 13 6.2 28.3 21.9 100.0
Scantron_Error 0 0.0 0.0 21.9 100.0
NA 164 78.1 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 45 14.3 16.5
No 228 72.4 83.5
Scantron_Error 0 0.0 0.0
NA 42 13.3 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 21 6.7 43.8 6.7 43.8
Two 17 5.4 35.4 12.1 79.2
Three_more 10 3.2 20.8 15.2 100.0
Scantron_Error 0 0.0 0.0 15.2 100.0
NA 267 84.8 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 133 42.2 58.1
No 96 30.5 41.9
Scantron_Error 0 0.0 0.0
NA 86 27.3 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 20 6.3 31.7 6.3 31.7
Two 18 5.7 28.6 12.1 60.3
Three_more 25 7.9 39.7 20.0 100.0
Scantron_Error 0 0.0 0.0 20.0 100.0
NA 252 80.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 42 11.8 13.8
No 263 73.9 86.2
Scantron_Error 0 0.0 0.0
NA 51 14.3 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 23 6.5 43.4 6.5 43.4
Two 18 5.1 34.0 11.5 77.4
Three_more 12 3.4 22.6 14.9 100.0
Scantron_Error 0 0.0 0.0 14.9 100.0
NA 303 85.1 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 141 39.6 59.2
No 93 26.1 39.1
Scantron_Error 4 1.1 1.7
NA 118 33.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 19 5.3 24.1 5.3 24.1
Two 30 8.4 38.0 13.8 62.0
Three_more 30 8.4 38.0 22.2 100.0
Scantron_Error 0 0.0 0.0 22.2 100.0
NA 277 77.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 60 10.3 12.6
No 416 71.1 87.0
Scantron_Error 2 0.3 0.4
NA 107 18.3 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 35 6.0 40.7 6.0 40.7
Two 25 4.3 29.1 10.3 69.8
Three_more 26 4.4 30.2 14.7 100.0
Scantron_Error 0 0.0 0.0 14.7 100.0
NA 499 85.3 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 233 39.8 59.1
No 159 27.2 40.4
Scantron_Error 2 0.3 0.5
NA 191 32.6 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 33 5.6 29.5 5.6 29.5
Two 35 6.0 31.2 11.6 60.7
Three_more 44 7.5 39.3 19.1 100.0
Scantron_Error 0 0.0 0.0 19.1 100.0
NA 473 80.9 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 235 13.4 15.8
No 1249 71.2 84.1
Scantron_Error 1 0.1 0.1
NA 269 15.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 130 7.4 51.4 7.4 51.4
Two 67 3.8 26.5 11.2 77.9
Three_more 56 3.2 22.1 14.4 100.0
Scantron_Error 0 0.0 0.0 14.4 100.0
NA 1501 85.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 757 43.2 65.1
No 398 22.7 34.2
Scantron_Error 8 0.5 0.7
NA 591 33.7 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 71 4.0 20.3 4.0 20.3
Two 145 8.3 41.5 12.3 61.9
Three_more 132 7.5 37.8 19.8 99.7
Scantron_Error 1 0.1 0.3 19.9 100.0
NA 1405 80.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 3 18.8 20
No 12 75.0 80
Scantron_Error 0 0.0 0
NA 1 6.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")
a. If yes, How many?
n % val% %cum val%cum
One 3 18.8 75 18.8 75
Two 1 6.2 25 25.0 100
Three_more 0 0.0 0 25.0 100
Scantron_Error 0 0.0 0 25.0 100
NA 12 75.0 NA 100.0 NA
Total 16 100.0 100 100.0 100
  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 9 56.2 69.2
No 4 25.0 30.8
Scantron_Error 0 0.0 0.0
NA 3 18.8 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")
b. If yes, How many?
n % val% %cum val%cum
One 1 6.2 50 6.2 50
Two 1 6.2 50 12.5 100
Three_more 0 0.0 0 12.5 100
Scantron_Error 0 0.0 0 12.5 100
NA 14 87.5 NA 100.0 NA
Total 16 100.0 100 100.0 100
rm(temp.dd) 

E3

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made

All data

  e3 <- as.factor(d[,"e3"])
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 521 14.6 15.0
With_family_or_friends 230 6.5 6.6
With_family_and_doctor 494 13.9 14.2
With_doctor 878 24.7 25.3
Doctor_made 1073 30.2 30.9
Dont_know_or_remember 204 5.7 5.9
Scantron_Error 75 2.1 2.2
NA 82 2.3 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 68 21.2 21.3
With_family_or_friends 13 4.0 4.1
With_family_and_doctor 34 10.6 10.7
With_doctor 85 26.5 26.6
Doctor_made 105 32.7 32.9
Dont_know_or_remember 12 3.7 3.8
Scantron_Error 2 0.6 0.6
NA 2 0.6 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 34 16.2 16.5
With_family_or_friends 13 6.2 6.3
With_family_and_doctor 28 13.3 13.6
With_doctor 56 26.7 27.2
Doctor_made 64 30.5 31.1
Dont_know_or_remember 5 2.4 2.4
Scantron_Error 6 2.9 2.9
NA 4 1.9 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 46 14.6 14.9
With_family_or_friends 19 6.0 6.2
With_family_and_doctor 41 13.0 13.3
With_doctor 78 24.8 25.3
Doctor_made 112 35.6 36.4
Dont_know_or_remember 7 2.2 2.3
Scantron_Error 5 1.6 1.6
NA 7 2.2 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 54 15.2 15.6
With_family_or_friends 23 6.5 6.6
With_family_and_doctor 51 14.3 14.7
With_doctor 78 21.9 22.5
Doctor_made 109 30.6 31.5
Dont_know_or_remember 23 6.5 6.6
Scantron_Error 8 2.2 2.3
NA 10 2.8 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 74 12.6 12.9
With_family_or_friends 51 8.7 8.9
With_family_and_doctor 80 13.7 14.0
With_doctor 133 22.7 23.3
Doctor_made 183 31.3 32.0
Dont_know_or_remember 39 6.7 6.8
Scantron_Error 12 2.1 2.1
NA 13 2.2 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 241 13.7 14.1
With_family_or_friends 109 6.2 6.4
With_family_and_doctor 259 14.8 15.2
With_doctor 445 25.4 26.1
Doctor_made 495 28.2 29.0
Dont_know_or_remember 118 6.7 6.9
Scantron_Error 41 2.3 2.4
NA 46 2.6 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 4 25.0 25.0
With_family_or_friends 2 12.5 12.5
With_family_and_doctor 1 6.2 6.2
With_doctor 3 18.8 18.8
Doctor_made 5 31.2 31.2
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 1 6.2 6.2
Total 16 100.0 100.0
rm(temp.dd) 

E4

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember

All data

  e4 <- as.factor(d[,"e4"])
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 1280 36.0 36.7
Intermediate_risk 594 16.7 17.0
High_risk 787 22.1 22.5
Unknown_risk 335 9.4 9.6
Dont_know_or_remember 488 13.7 14.0
Scantron_Error 7 0.2 0.2
NA 66 1.9 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 128 39.9 40.4
Intermediate_risk 50 15.6 15.8
High_risk 73 22.7 23.0
Unknown_risk 31 9.7 9.8
Dont_know_or_remember 35 10.9 11.0
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 88 41.9 42.1
Intermediate_risk 51 24.3 24.4
High_risk 39 18.6 18.7
Unknown_risk 13 6.2 6.2
Dont_know_or_remember 17 8.1 8.1
Scantron_Error 1 0.5 0.5
NA 1 0.5 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 97 30.8 31.5
Intermediate_risk 58 18.4 18.8
High_risk 102 32.4 33.1
Unknown_risk 27 8.6 8.8
Dont_know_or_remember 22 7.0 7.1
Scantron_Error 2 0.6 0.6
NA 7 2.2 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 113 31.7 32.1
Intermediate_risk 52 14.6 14.8
High_risk 92 25.8 26.1
Unknown_risk 51 14.3 14.5
Dont_know_or_remember 44 12.4 12.5
Scantron_Error 0 0.0 0.0
NA 4 1.1 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 202 34.5 34.9
Intermediate_risk 91 15.6 15.7
High_risk 114 19.5 19.7
Unknown_risk 65 11.1 11.2
Dont_know_or_remember 105 17.9 18.2
Scantron_Error 1 0.2 0.2
NA 7 1.2 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 647 36.9 37.8
Intermediate_risk 290 16.5 16.9
High_risk 363 20.7 21.2
Unknown_risk 145 8.3 8.5
Dont_know_or_remember 263 15.0 15.4
Scantron_Error 3 0.2 0.2
NA 43 2.5 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 5 31.2 31.2
Intermediate_risk 2 12.5 12.5
High_risk 4 25.0 25.0
Unknown_risk 3 18.8 18.8
Dont_know_or_remember 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
rm(temp.dd) 

E5

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know

All data

  e5 <- as.factor(d[,"e5"])
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88",
                     Scantron_Error="*")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 689 19.4 20.1
Seven 580 16.3 16.9
Eight_to_ten 516 14.5 15.0
Dont_know 1644 46.2 47.9
Scantron_Error 6 0.2 0.2
NA 122 3.4 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 57 17.8 18.1
Seven 52 16.2 16.5
Eight_to_ten 62 19.3 19.7
Dont_know 144 44.9 45.7
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 48 22.9 23.4
Seven 43 20.5 21.0
Eight_to_ten 29 13.8 14.1
Dont_know 84 40.0 41.0
Scantron_Error 1 0.5 0.5
NA 5 2.4 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 67 21.3 22.1
Seven 61 19.4 20.1
Eight_to_ten 57 18.1 18.8
Dont_know 118 37.5 38.9
Scantron_Error 0 0.0 0.0
NA 12 3.8 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 53 14.9 15.3
Seven 63 17.7 18.2
Eight_to_ten 47 13.2 13.6
Dont_know 183 51.4 52.9
Scantron_Error 0 0.0 0.0
NA 10 2.8 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 95 16.2 16.8
Seven 88 15.0 15.6
Eight_to_ten 63 10.8 11.2
Dont_know 317 54.2 56.2
Scantron_Error 1 0.2 0.2
NA 21 3.6 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 364 20.8 21.6
Seven 271 15.5 16.1
Eight_to_ten 255 14.5 15.1
Dont_know 793 45.2 47.0
Scantron_Error 4 0.2 0.2
NA 67 3.8 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 5 31.2 33.3
Seven 2 12.5 13.3
Eight_to_ten 3 18.8 20.0
Dont_know 5 31.2 33.3
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

E6

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage

All data

  e6 <- as.factor(d[,"e6"])
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88",
                     Scantron_Error="*")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 2451 68.9 71.0
Regional 149 4.2 4.3
Distant 67 1.9 1.9
Dont_know 782 22.0 22.7
Scantron_Error 3 0.1 0.1
NA 105 3.0 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 236 73.5 74.7
Regional 17 5.3 5.4
Distant 5 1.6 1.6
Dont_know 58 18.1 18.4
Scantron_Error 0 0.0 0.0
NA 5 1.6 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 167 79.5 81.1
Regional 2 1.0 1.0
Distant 7 3.3 3.4
Dont_know 30 14.3 14.6
Scantron_Error 0 0.0 0.0
NA 4 1.9 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 229 72.7 75.1
Regional 19 6.0 6.2
Distant 9 2.9 3.0
Dont_know 48 15.2 15.7
Scantron_Error 0 0.0 0.0
NA 10 3.2 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 222 62.4 64.7
Regional 18 5.1 5.2
Distant 11 3.1 3.2
Dont_know 91 25.6 26.5
Scantron_Error 1 0.3 0.3
NA 13 3.7 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 378 64.6 66.5
Regional 20 3.4 3.5
Distant 5 0.9 0.9
Dont_know 165 28.2 29.0
Scantron_Error 0 0.0 0.0
NA 17 2.9 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 1208 68.9 71.1
Regional 72 4.1 4.2
Distant 30 1.7 1.8
Dont_know 386 22.0 22.7
Scantron_Error 2 0.1 0.1
NA 56 3.2 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 11 68.8 68.8
Regional 1 6.2 6.2
Distant 0 0.0 0.0
Dont_know 4 25.0 25.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
rm(temp.dd) 

E7

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know

All data

  e7 <- as.factor(d[,"e7"])
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e7 <- ordered(e7, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 1280 36.0 37.2
Yes 1328 37.3 38.6
Dont_know 826 23.2 24.0
Scantron_Error 5 0.1 0.1
NA 118 3.3 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 143 44.5 45.4
Yes 116 36.1 36.8
Dont_know 56 17.4 17.8
Scantron_Error 0 0.0 0.0
NA 6 1.9 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 86 41.0 41.5
Yes 90 42.9 43.5
Dont_know 31 14.8 15.0
Scantron_Error 0 0.0 0.0
NA 3 1.4 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 148 47.0 49.2
Yes 116 36.8 38.5
Dont_know 37 11.7 12.3
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 120 33.7 35.1
Yes 121 34.0 35.4
Dont_know 100 28.1 29.2
Scantron_Error 1 0.3 0.3
NA 14 3.9 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 173 29.6 30.6
Yes 235 40.2 41.6
Dont_know 156 26.7 27.6
Scantron_Error 1 0.2 0.2
NA 20 3.4 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 600 34.2 35.4
Yes 646 36.8 38.2
Dont_know 444 25.3 26.2
Scantron_Error 3 0.2 0.2
NA 61 3.5 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 10 62.5 62.5
Yes 4 25.0 25.0
Dont_know 2 12.5 12.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
rm(temp.dd) 

E8

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made

All data

  e8 <- as.factor(d[,"e8"])
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 466 13.1 13.7
With_family_or_friends 506 14.2 14.8
With_family_and_doctor 1278 35.9 37.5
With_doctor 802 22.5 23.5
Doctor_made 310 8.7 9.1
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 49 1.4 1.4
NA 146 4.1 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 47 14.6 15.1
With_family_or_friends 39 12.1 12.5
With_family_and_doctor 116 36.1 37.2
With_doctor 72 22.4 23.1
Doctor_made 34 10.6 10.9
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 4 1.2 1.3
NA 9 2.8 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 23 11.0 11.2
With_family_or_friends 28 13.3 13.7
With_family_and_doctor 86 41.0 42.0
With_doctor 49 23.3 23.9
Doctor_made 16 7.6 7.8
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 3 1.4 1.5
NA 5 2.4 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 30 9.5 9.8
With_family_or_friends 43 13.7 14.1
With_family_and_doctor 112 35.6 36.7
With_doctor 82 26.0 26.9
Doctor_made 31 9.8 10.2
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 7 2.2 2.3
NA 10 3.2 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 56 15.7 16.7
With_family_or_friends 50 14.0 14.9
With_family_and_doctor 115 32.3 34.3
With_doctor 72 20.2 21.5
Doctor_made 36 10.1 10.7
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 6 1.7 1.8
NA 21 5.9 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 102 17.4 18.2
With_family_or_friends 82 14.0 14.6
With_family_and_doctor 188 32.1 33.6
With_doctor 121 20.7 21.6
Doctor_made 63 10.8 11.2
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 4 0.7 0.7
NA 25 4.3 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 206 11.7 12.3
With_family_or_friends 263 15.0 15.7
With_family_and_doctor 657 37.5 39.1
With_doctor 400 22.8 23.8
Doctor_made 130 7.4 7.7
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 24 1.4 1.4
NA 74 4.2 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 2 12.5 14.3
With_family_or_friends 1 6.2 7.1
With_family_and_doctor 4 25.0 28.6
With_doctor 6 37.5 42.9
Doctor_made 0 0.0 0.0
Dont_know_or_remember 0 0.0 0.0
Scantron_Error 1 6.2 7.1
NA 2 12.5 NA
Total 16 100.0 100.0
rm(temp.dd) 

E9

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer

All data

  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 3064 86.1 100
Scantron_Error 0 0.0 0
NA 493 13.9 NA
Total 3557 100.0 100
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 1041 29.3 100
Scantron_Error 0 0.0 0
NA 2516 70.7 NA
Total 3557 100.0 100
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 933 26.2 100
Scantron_Error 0 0.0 0
NA 2624 73.8 NA
Total 3557 100.0 100
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 447 12.6 100
Scantron_Error 0 0.0 0
NA 3110 87.4 NA
Total 3557 100.0 100
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 213 6 100
Scantron_Error 0 0 0
NA 3344 94 NA
Total 3557 100 100
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 376 10.6 100
Scantron_Error 0 0.0 0
NA 3181 89.4 NA
Total 3557 100.0 100
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 678 19.1 100
Scantron_Error 0 0.0 0
NA 2879 80.9 NA
Total 3557 100.0 100
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 284 8 100
Scantron_Error 0 0 0
NA 3273 92 NA
Total 3557 100 100
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 1518 42.7 100
Scantron_Error 0 0.0 0
NA 2039 57.3 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 294 91.6 100
Scantron_Error 0 0.0 0
NA 27 8.4 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 94 29.3 100
Scantron_Error 0 0.0 0
NA 227 70.7 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 86 26.8 100
Scantron_Error 0 0.0 0
NA 235 73.2 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 31 9.7 100
Scantron_Error 0 0.0 0
NA 290 90.3 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 12 3.7 100
Scantron_Error 0 0.0 0
NA 309 96.3 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 22 6.9 100
Scantron_Error 0 0.0 0
NA 299 93.1 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 45 14 100
Scantron_Error 0 0 0
NA 276 86 NA
Total 321 100 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 23 7.2 100
Scantron_Error 0 0.0 0
NA 298 92.8 NA
Total 321 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 172 53.6 100
Scantron_Error 0 0.0 0
NA 149 46.4 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 185 88.1 100
Scantron_Error 0 0.0 0
NA 25 11.9 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 71 33.8 100
Scantron_Error 0 0.0 0
NA 139 66.2 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 64 30.5 100
Scantron_Error 0 0.0 0
NA 146 69.5 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 38 18.1 100
Scantron_Error 0 0.0 0
NA 172 81.9 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 8 3.8 100
Scantron_Error 0 0.0 0
NA 202 96.2 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 18 8.6 100
Scantron_Error 0 0.0 0
NA 192 91.4 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 40 19 100
Scantron_Error 0 0 0
NA 170 81 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 16 7.6 100
Scantron_Error 0 0.0 0
NA 194 92.4 NA
Total 210 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 81 38.6 100
Scantron_Error 0 0.0 0
NA 129 61.4 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 266 84.4 100
Scantron_Error 0 0.0 0
NA 49 15.6 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 100 31.7 100
Scantron_Error 0 0.0 0
NA 215 68.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 90 28.6 100
Scantron_Error 0 0.0 0
NA 225 71.4 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 44 14 100
Scantron_Error 0 0 0
NA 271 86 NA
Total 315 100 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 11 3.5 100
Scantron_Error 0 0.0 0
NA 304 96.5 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 24 7.6 100
Scantron_Error 0 0.0 0
NA 291 92.4 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 56 17.8 100
Scantron_Error 0 0.0 0
NA 259 82.2 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 27 8.6 100
Scantron_Error 0 0.0 0
NA 288 91.4 NA
Total 315 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 152 48.3 100
Scantron_Error 0 0.0 0
NA 163 51.7 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 303 85.1 100
Scantron_Error 0 0.0 0
NA 53 14.9 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 106 29.8 100
Scantron_Error 0 0.0 0
NA 250 70.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 86 24.2 100
Scantron_Error 0 0.0 0
NA 270 75.8 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 45 12.6 100
Scantron_Error 0 0.0 0
NA 311 87.4 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 22 6.2 100
Scantron_Error 0 0.0 0
NA 334 93.8 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 35 9.8 100
Scantron_Error 0 0.0 0
NA 321 90.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 68 19.1 100
Scantron_Error 0 0.0 0
NA 288 80.9 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 27 7.6 100
Scantron_Error 0 0.0 0
NA 329 92.4 NA
Total 356 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 140 39.3 100
Scantron_Error 0 0.0 0
NA 216 60.7 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 506 86.5 100
Scantron_Error 0 0.0 0
NA 79 13.5 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 124 21.2 100
Scantron_Error 0 0.0 0
NA 461 78.8 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 123 21 100
Scantron_Error 0 0 0
NA 462 79 NA
Total 585 100 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 49 8.4 100
Scantron_Error 0 0.0 0
NA 536 91.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 42 7.2 100
Scantron_Error 0 0.0 0
NA 543 92.8 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 63 10.8 100
Scantron_Error 0 0.0 0
NA 522 89.2 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 98 16.8 100
Scantron_Error 0 0.0 0
NA 487 83.2 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 41 7 100
Scantron_Error 0 0 0
NA 544 93 NA
Total 585 100 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 232 39.7 100
Scantron_Error 0 0.0 0
NA 353 60.3 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 1497 85.3 100
Scantron_Error 0 0.0 0
NA 257 14.7 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 538 30.7 100
Scantron_Error 0 0.0 0
NA 1216 69.3 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 476 27.1 100
Scantron_Error 0 0.0 0
NA 1278 72.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 236 13.5 100
Scantron_Error 0 0.0 0
NA 1518 86.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 117 6.7 100
Scantron_Error 0 0.0 0
NA 1637 93.3 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 213 12.1 100
Scantron_Error 0 0.0 0
NA 1541 87.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 366 20.9 100
Scantron_Error 0 0.0 0
NA 1388 79.1 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 147 8.4 100
Scantron_Error 0 0.0 0
NA 1607 91.6 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 731 41.7 100
Scantron_Error 0 0.0 0
NA 1023 58.3 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 13 81.2 100
Scantron_Error 0 0.0 0
NA 3 18.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 8 50 100
Scantron_Error 0 0 0
NA 8 50 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 8 50 100
Scantron_Error 0 0 0
NA 8 50 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 4 25 100
Scantron_Error 0 0 0
NA 12 75 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 5 31.2 100
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 3 18.8 100
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 515 14.5 100
Scantron_Error 0 0.0 0
NA 3042 85.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 10 62.5 100
Scantron_Error 0 0.0 0
NA 6 37.5 NA
Total 16 100.0 100
rm(temp.dd) 

E10

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))

All data

  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 201 5.7 100
Scantron_Error 0 0.0 0
NA 3356 94.3 NA
Total 3557 100.0 100
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 569 16 100
Scantron_Error 0 0 0
NA 2988 84 NA
Total 3557 100 100
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 1133 31.9 100
Scantron_Error 0 0.0 0
NA 2424 68.1 NA
Total 3557 100.0 100
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 1396 39.2 100
Scantron_Error 0 0.0 0
NA 2161 60.8 NA
Total 3557 100.0 100
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 443 12.5 100
Scantron_Error 0 0.0 0
NA 3114 87.5 NA
Total 3557 100.0 100
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 37 1 100
Scantron_Error 0 0 0
NA 3520 99 NA
Total 3557 100 100
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 115 3.2 100
Scantron_Error 0 0.0 0
NA 3442 96.8 NA
Total 3557 100.0 100
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 192 5.4 100
Scantron_Error 0 0.0 0
NA 3365 94.6 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 18 5.6 100
Scantron_Error 0 0.0 0
NA 303 94.4 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 52 16.2 100
Scantron_Error 0 0.0 0
NA 269 83.8 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 150 46.7 100
Scantron_Error 0 0.0 0
NA 171 53.3 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 114 35.5 100
Scantron_Error 0 0.0 0
NA 207 64.5 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 56 17.4 100
Scantron_Error 0 0.0 0
NA 265 82.6 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 2 0.6 100
Scantron_Error 0 0.0 0
NA 319 99.4 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 13 4 100
Scantron_Error 0 0 0
NA 308 96 NA
Total 321 100 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 10 3.1 100
Scantron_Error 0 0.0 0
NA 311 96.9 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 9 4.3 100
Scantron_Error 0 0.0 0
NA 201 95.7 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 46 21.9 100
Scantron_Error 0 0.0 0
NA 164 78.1 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 51 24.3 100
Scantron_Error 0 0.0 0
NA 159 75.7 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 84 40 100
Scantron_Error 0 0 0
NA 126 60 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 28 13.3 100
Scantron_Error 0 0.0 0
NA 182 86.7 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 2 1 100
Scantron_Error 0 0 0
NA 208 99 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 5 2.4 100
Scantron_Error 0 0.0 0
NA 205 97.6 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 12 5.7 100
Scantron_Error 0 0.0 0
NA 198 94.3 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 21 6.7 100
Scantron_Error 0 0.0 0
NA 294 93.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 60 19 100
Scantron_Error 0 0 0
NA 255 81 NA
Total 315 100 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 133 42.2 100
Scantron_Error 0 0.0 0
NA 182 57.8 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 108 34.3 100
Scantron_Error 0 0.0 0
NA 207 65.7 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 52 16.5 100
Scantron_Error 0 0.0 0
NA 263 83.5 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 4 1.3 100
Scantron_Error 0 0.0 0
NA 311 98.7 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 9 2.9 100
Scantron_Error 0 0.0 0
NA 306 97.1 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 10 3.2 100
Scantron_Error 0 0.0 0
NA 305 96.8 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 25 7 100
Scantron_Error 0 0 0
NA 331 93 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 55 15.4 100
Scantron_Error 0 0.0 0
NA 301 84.6 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 124 34.8 100
Scantron_Error 0 0.0 0
NA 232 65.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 135 37.9 100
Scantron_Error 0 0.0 0
NA 221 62.1 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 55 15.4 100
Scantron_Error 0 0.0 0
NA 301 84.6 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 4 1.1 100
Scantron_Error 0 0.0 0
NA 352 98.9 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 15 4.2 100
Scantron_Error 0 0.0 0
NA 341 95.8 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 17 4.8 100
Scantron_Error 0 0.0 0
NA 339 95.2 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 39 6.7 100
Scantron_Error 0 0.0 0
NA 546 93.3 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 74 12.6 100
Scantron_Error 0 0.0 0
NA 511 87.4 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 186 31.8 100
Scantron_Error 0 0.0 0
NA 399 68.2 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 201 34.4 100
Scantron_Error 0 0.0 0
NA 384 65.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 55 9.4 100
Scantron_Error 0 0.0 0
NA 530 90.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 7 1.2 100
Scantron_Error 0 0.0 0
NA 578 98.8 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 21 3.6 100
Scantron_Error 0 0.0 0
NA 564 96.4 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 38 6.5 100
Scantron_Error 0 0.0 0
NA 547 93.5 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 89 5.1 100
Scantron_Error 0 0.0 0
NA 1665 94.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 279 15.9 100
Scantron_Error 0 0.0 0
NA 1475 84.1 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 483 27.5 100
Scantron_Error 0 0.0 0
NA 1271 72.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 746 42.5 100
Scantron_Error 0 0.0 0
NA 1008 57.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 195 11.1 100
Scantron_Error 0 0.0 0
NA 1559 88.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 18 1 100
Scantron_Error 0 0 0
NA 1736 99 NA
Total 1754 100 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 51 2.9 100
Scantron_Error 0 0.0 0
NA 1703 97.1 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 103 5.9 100
Scantron_Error 0 0.0 0
NA 1651 94.1 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 3 18.8 100
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 6 37.5 100
Scantron_Error 0 0.0 0
NA 10 62.5 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 8 50 100
Scantron_Error 0 0 0
NA 8 50 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 2 12.5 100
Scantron_Error 0 0.0 0
NA 14 87.5 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 2 12.5 100
Scantron_Error 0 0.0 0
NA 14 87.5 NA
Total 16 100.0 100
rm(temp.dd) 

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type

All data

  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 1174 33 100
Scantron_Error 0 0 0
NA 2383 67 NA
Total 3557 100 100
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 194 5.5 100
Scantron_Error 0 0.0 0
NA 3363 94.5 NA
Total 3557 100.0 100
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 226 6.4 100
Scantron_Error 0 0.0 0
NA 3331 93.6 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 165 51.4 100
Scantron_Error 0 0.0 0
NA 156 48.6 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 7 2.2 100
Scantron_Error 0 0.0 0
NA 314 97.8 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 8 2.5 100
Scantron_Error 0 0.0 0
NA 313 97.5 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 46 21.9 100
Scantron_Error 0 0.0 0
NA 164 78.1 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 9 4.3 100
Scantron_Error 0 0.0 0
NA 201 95.7 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 10 4.8 100
Scantron_Error 0 0.0 0
NA 200 95.2 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 151 47.9 100
Scantron_Error 0 0.0 0
NA 164 52.1 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 11 3.5 100
Scantron_Error 0 0.0 0
NA 304 96.5 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 13 4.1 100
Scantron_Error 0 0.0 0
NA 302 95.9 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 121 34 100
Scantron_Error 0 0 0
NA 235 66 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 32 9 100
Scantron_Error 0 0 0
NA 324 91 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 15 4.2 100
Scantron_Error 0 0.0 0
NA 341 95.8 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 206 35.2 100
Scantron_Error 0 0.0 0
NA 379 64.8 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 47 8 100
Scantron_Error 0 0 0
NA 538 92 NA
Total 585 100 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 53 9.1 100
Scantron_Error 0 0.0 0
NA 532 90.9 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 476 27.1 100
Scantron_Error 0 0.0 0
NA 1278 72.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 87 5 100
Scantron_Error 0 0 0
NA 1667 95 NA
Total 1754 100 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 127 7.2 100
Scantron_Error 0 0.0 0
NA 1627 92.8 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 9 56.2 100
Scantron_Error 0 0.0 0
NA 7 43.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
rm(temp.dd) 

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type

All data

  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 1268 35.6 100
Scantron_Error 0 0.0 0
NA 2289 64.4 NA
Total 3557 100.0 100
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 633 17.8 100
Scantron_Error 0 0.0 0
NA 2924 82.2 NA
Total 3557 100.0 100
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 214 6 100
Scantron_Error 0 0 0
NA 3343 94 NA
Total 3557 100 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 115 35.8 100
Scantron_Error 0 0.0 0
NA 206 64.2 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 24 7.5 100
Scantron_Error 0 0.0 0
NA 297 92.5 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 13 4 100
Scantron_Error 0 0 0
NA 308 96 NA
Total 321 100 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 78 37.1 100
Scantron_Error 0 0.0 0
NA 132 62.9 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 47 22.4 100
Scantron_Error 0 0.0 0
NA 163 77.6 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 12 5.7 100
Scantron_Error 0 0.0 0
NA 198 94.3 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 95 30.2 100
Scantron_Error 0 0.0 0
NA 220 69.8 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 34 10.8 100
Scantron_Error 0 0.0 0
NA 281 89.2 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 13 4.1 100
Scantron_Error 0 0.0 0
NA 302 95.9 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 124 34.8 100
Scantron_Error 0 0.0 0
NA 232 65.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 28 7.9 100
Scantron_Error 0 0.0 0
NA 328 92.1 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 24 6.7 100
Scantron_Error 0 0.0 0
NA 332 93.3 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 209 35.7 100
Scantron_Error 0 0.0 0
NA 376 64.3 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 50 8.5 100
Scantron_Error 0 0.0 0
NA 535 91.5 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 43 7.4 100
Scantron_Error 0 0.0 0
NA 542 92.6 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 640 36.5 100
Scantron_Error 0 0.0 0
NA 1114 63.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 448 25.5 100
Scantron_Error 0 0.0 0
NA 1306 74.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 108 6.2 100
Scantron_Error 0 0.0 0
NA 1646 93.8 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 7 43.8 100
Scantron_Error 0 0.0 0
NA 9 56.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 2 12.5 100
Scantron_Error 0 0.0 0
NA 14 87.5 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
rm(temp.dd) 

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type

All data

  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 622 17.5 100
Scantron_Error 0 0.0 0
NA 2935 82.5 NA
Total 3557 100.0 100
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 46 1.3 100
Scantron_Error 0 0.0 0
NA 3511 98.7 NA
Total 3557 100.0 100
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 78 2.2 100
Scantron_Error 0 0.0 0
NA 3479 97.8 NA
Total 3557 100.0 100
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 56 1.6 100
Scantron_Error 0 0.0 0
NA 3501 98.4 NA
Total 3557 100.0 100
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 182 5.1 100
Scantron_Error 0 0.0 0
NA 3375 94.9 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 72 22.4 100
Scantron_Error 0 0.0 0
NA 249 77.6 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 1 0.3 100
Scantron_Error 0 0.0 0
NA 320 99.7 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 11 3.4 100
Scantron_Error 0 0.0 0
NA 310 96.6 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 5 1.6 100
Scantron_Error 0 0.0 0
NA 316 98.4 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 10 3.1 100
Scantron_Error 0 0.0 0
NA 311 96.9 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 53 25.2 100
Scantron_Error 0 0.0 0
NA 157 74.8 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 0 0 NaN
Scantron_Error 0 0 NaN
NA 210 100 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 3 1.4 100
Scantron_Error 0 0.0 0
NA 207 98.6 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 2 1 100
Scantron_Error 0 0 0
NA 208 99 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 5 2.4 100
Scantron_Error 0 0.0 0
NA 205 97.6 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 62 19.7 100
Scantron_Error 0 0.0 0
NA 253 80.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 3 1 100
Scantron_Error 0 0 0
NA 312 99 NA
Total 315 100 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 14 4.4 100
Scantron_Error 0 0.0 0
NA 301 95.6 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 8 2.5 100
Scantron_Error 0 0.0 0
NA 307 97.5 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 12 3.8 100
Scantron_Error 0 0.0 0
NA 303 96.2 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 64 18 100
Scantron_Error 0 0 0
NA 292 82 NA
Total 356 100 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 6 1.7 100
Scantron_Error 0 0.0 0
NA 350 98.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 6 1.7 100
Scantron_Error 0 0.0 0
NA 350 98.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 5 1.4 100
Scantron_Error 0 0.0 0
NA 351 98.6 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 21 5.9 100
Scantron_Error 0 0.0 0
NA 335 94.1 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 99 16.9 100
Scantron_Error 0 0.0 0
NA 486 83.1 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 11 1.9 100
Scantron_Error 0 0.0 0
NA 574 98.1 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 8 1.4 100
Scantron_Error 0 0.0 0
NA 577 98.6 NA
Total 585 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 6 1 100
Scantron_Error 0 0 0
NA 579 99 NA
Total 585 100 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 44 7.5 100
Scantron_Error 0 0.0 0
NA 541 92.5 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 269 15.3 100
Scantron_Error 0 0.0 0
NA 1485 84.7 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 25 1.4 100
Scantron_Error 0 0.0 0
NA 1729 98.6 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 36 2.1 100
Scantron_Error 0 0.0 0
NA 1718 97.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 30 1.7 100
Scantron_Error 0 0.0 0
NA 1724 98.3 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 90 5.1 100
Scantron_Error 0 0.0 0
NA 1664 94.9 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 3 18.8 100
Scantron_Error 0 0.0 0
NA 13 81.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
rm(temp.dd) 

E11

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much

All data

  e11a <- as.factor(d[,"e11a"])
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 111 3.1 3.3
A_little_bit 126 3.5 3.7
Somewhat 415 11.7 12.2
Quite_a_bit 874 24.6 25.7
Very_much 1865 52.4 54.9
Scantron_Error 6 0.2 0.2
NA 160 4.5 NA
Total 3557 100.0 100.0
  e11b <- as.factor(d[,"e11b"])
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 82 2.3 2.4
A_little_bit 140 3.9 4.1
Somewhat 407 11.4 11.9
Quite_a_bit 834 23.4 24.5
Very_much 1940 54.5 56.9
Scantron_Error 4 0.1 0.1
NA 150 4.2 NA
Total 3557 100.0 100.0
  e11c <- as.factor(d[,"e11c"])
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 336 9.4 9.9
A_little_bit 405 11.4 11.9
Somewhat 1123 31.6 33.0
Quite_a_bit 668 18.8 19.6
Very_much 856 24.1 25.2
Scantron_Error 12 0.3 0.4
NA 157 4.4 NA
Total 3557 100.0 100.0
  e11d <- as.factor(d[,"e11d"])
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 87 2.4 2.6
A_little_bit 141 4.0 4.1
Somewhat 529 14.9 15.6
Quite_a_bit 847 23.8 24.9
Very_much 1787 50.2 52.6
Scantron_Error 7 0.2 0.2
NA 159 4.5 NA
Total 3557 100.0 100.0
  e11e <- as.factor(d[,"e11e"])
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 137 3.9 4.0
A_little_bit 125 3.5 3.7
Somewhat 413 11.6 12.1
Quite_a_bit 518 14.6 15.2
Very_much 2205 62.0 64.8
Scantron_Error 5 0.1 0.1
NA 154 4.3 NA
Total 3557 100.0 100.0
  e11f <- as.factor(d[,"e11f"])
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 191 5.4 5.7
A_little_bit 133 3.7 3.9
Somewhat 495 13.9 14.7
Quite_a_bit 461 13.0 13.7
Very_much 2092 58.8 62.0
Scantron_Error 4 0.1 0.1
NA 181 5.1 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 9 2.8 2.8
A_little_bit 19 5.9 6.0
Somewhat 45 14.0 14.2
Quite_a_bit 102 31.8 32.2
Very_much 142 44.2 44.8
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 6 1.9 1.9
A_little_bit 18 5.6 5.7
Somewhat 44 13.7 13.9
Quite_a_bit 97 30.2 30.6
Very_much 152 47.4 47.9
Scantron_Error 0 0.0 0.0
NA 4 1.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 33 10.3 10.4
A_little_bit 55 17.1 17.3
Somewhat 99 30.8 31.1
Quite_a_bit 73 22.7 23.0
Very_much 58 18.1 18.2
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 11 3.4 3.5
A_little_bit 9 2.8 2.8
Somewhat 53 16.5 16.7
Quite_a_bit 98 30.5 30.8
Very_much 147 45.8 46.2
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 13 4.0 4.1
A_little_bit 8 2.5 2.5
Somewhat 39 12.1 12.3
Quite_a_bit 66 20.6 20.8
Very_much 192 59.8 60.4
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 13 4.0 4.1
A_little_bit 18 5.6 5.7
Somewhat 63 19.6 19.8
Quite_a_bit 64 19.9 20.1
Very_much 160 49.8 50.3
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 5 2.4 2.5
A_little_bit 2 1.0 1.0
Somewhat 24 11.4 11.8
Quite_a_bit 52 24.8 25.5
Very_much 121 57.6 59.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 5 2.4 2.5
A_little_bit 4 1.9 2.0
Somewhat 23 11.0 11.3
Quite_a_bit 47 22.4 23.0
Very_much 125 59.5 61.3
Scantron_Error 0 0.0 0.0
NA 6 2.9 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 15 7.1 7.4
A_little_bit 22 10.5 10.8
Somewhat 74 35.2 36.5
Quite_a_bit 40 19.0 19.7
Very_much 52 24.8 25.6
Scantron_Error 0 0.0 0.0
NA 7 3.3 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 2 1.0 1.0
A_little_bit 7 3.3 3.5
Somewhat 32 15.2 15.8
Quite_a_bit 44 21.0 21.8
Very_much 117 55.7 57.9
Scantron_Error 0 0.0 0.0
NA 8 3.8 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 7 3.3 3.4
A_little_bit 10 4.8 4.9
Somewhat 28 13.3 13.7
Quite_a_bit 23 11.0 11.2
Very_much 137 65.2 66.8
Scantron_Error 0 0.0 0.0
NA 5 2.4 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 9 4.3 4.4
A_little_bit 6 2.9 3.0
Somewhat 33 15.7 16.3
Quite_a_bit 23 11.0 11.3
Very_much 131 62.4 64.5
Scantron_Error 1 0.5 0.5
NA 7 3.3 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 11 3.5 3.7
A_little_bit 8 2.5 2.7
Somewhat 36 11.4 12.0
Quite_a_bit 71 22.5 23.7
Very_much 174 55.2 58.0
Scantron_Error 0 0.0 0.0
NA 15 4.8 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 7 2.2 2.3
A_little_bit 11 3.5 3.6
Somewhat 41 13.0 13.5
Quite_a_bit 78 24.8 25.7
Very_much 166 52.7 54.8
Scantron_Error 0 0.0 0.0
NA 12 3.8 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 31 9.8 10.3
A_little_bit 36 11.4 12.0
Somewhat 97 30.8 32.2
Quite_a_bit 56 17.8 18.6
Very_much 81 25.7 26.9
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 10 3.2 3.3
A_little_bit 20 6.3 6.6
Somewhat 49 15.6 16.3
Quite_a_bit 86 27.3 28.6
Very_much 136 43.2 45.2
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 14 4.4 4.7
A_little_bit 9 2.9 3.0
Somewhat 44 14.0 14.6
Quite_a_bit 49 15.6 16.3
Very_much 185 58.7 61.5
Scantron_Error 0 0.0 0.0
NA 14 4.4 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 23 7.3 7.8
A_little_bit 15 4.8 5.1
Somewhat 41 13.0 13.9
Quite_a_bit 47 14.9 16.0
Very_much 168 53.3 57.1
Scantron_Error 0 0.0 0.0
NA 21 6.7 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 12 3.4 3.5
A_little_bit 14 3.9 4.1
Somewhat 48 13.5 14.2
Quite_a_bit 83 23.3 24.5
Very_much 182 51.1 53.7
Scantron_Error 0 0.0 0.0
NA 17 4.8 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 10 2.8 2.9
A_little_bit 15 4.2 4.4
Somewhat 52 14.6 15.3
Quite_a_bit 76 21.3 22.4
Very_much 186 52.2 54.9
Scantron_Error 0 0.0 0.0
NA 17 4.8 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 37 10.4 10.9
A_little_bit 50 14.0 14.7
Somewhat 106 29.8 31.2
Quite_a_bit 59 16.6 17.4
Very_much 86 24.2 25.3
Scantron_Error 2 0.6 0.6
NA 16 4.5 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 9 2.5 2.7
A_little_bit 16 4.5 4.7
Somewhat 56 15.7 16.6
Quite_a_bit 89 25.0 26.3
Very_much 167 46.9 49.4
Scantron_Error 1 0.3 0.3
NA 18 5.1 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 11 3.1 3.2
A_little_bit 15 4.2 4.4
Somewhat 47 13.2 13.9
Quite_a_bit 61 17.1 18.0
Very_much 205 57.6 60.5
Scantron_Error 0 0.0 0.0
NA 17 4.8 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 18 5.1 5.4
A_little_bit 16 4.5 4.8
Somewhat 59 16.6 17.6
Quite_a_bit 50 14.0 14.9
Very_much 192 53.9 57.3
Scantron_Error 0 0.0 0.0
NA 21 5.9 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 20 3.4 3.6
A_little_bit 30 5.1 5.4
Somewhat 66 11.3 11.9
Quite_a_bit 144 24.6 25.9
Very_much 295 50.4 53.1
Scantron_Error 1 0.2 0.2
NA 29 5.0 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 13 2.2 2.3
A_little_bit 34 5.8 6.1
Somewhat 54 9.2 9.7
Quite_a_bit 126 21.5 22.6
Very_much 330 56.4 59.2
Scantron_Error 0 0.0 0.0
NA 28 4.8 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 68 11.6 12.2
A_little_bit 60 10.3 10.8
Somewhat 168 28.7 30.2
Quite_a_bit 103 17.6 18.5
Very_much 156 26.7 28.1
Scantron_Error 1 0.2 0.2
NA 29 5.0 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 15 2.6 2.7
A_little_bit 19 3.2 3.4
Somewhat 91 15.6 16.4
Quite_a_bit 128 21.9 23.1
Very_much 301 51.5 54.2
Scantron_Error 1 0.2 0.2
NA 30 5.1 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 19 3.2 3.4
A_little_bit 20 3.4 3.6
Somewhat 62 10.6 11.2
Quite_a_bit 84 14.4 15.1
Very_much 370 63.2 66.5
Scantron_Error 1 0.2 0.2
NA 29 5.0 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 28 4.8 5.1
A_little_bit 20 3.4 3.6
Somewhat 75 12.8 13.6
Quite_a_bit 58 9.9 10.5
Very_much 370 63.2 67.2
Scantron_Error 0 0.0 0.0
NA 34 5.8 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 52 3.0 3.1
A_little_bit 53 3.0 3.2
Somewhat 195 11.1 11.7
Quite_a_bit 415 23.7 24.9
Very_much 945 53.9 56.8
Scantron_Error 5 0.3 0.3
NA 89 5.1 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 40 2.3 2.4
A_little_bit 58 3.3 3.5
Somewhat 190 10.8 11.4
Quite_a_bit 406 23.1 24.3
Very_much 973 55.5 58.2
Scantron_Error 4 0.2 0.2
NA 83 4.7 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 149 8.5 8.9
A_little_bit 180 10.3 10.8
Somewhat 574 32.7 34.5
Quite_a_bit 334 19.0 20.0
Very_much 420 23.9 25.2
Scantron_Error 9 0.5 0.5
NA 88 5.0 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 38 2.2 2.3
A_little_bit 69 3.9 4.1
Somewhat 246 14.0 14.7
Quite_a_bit 399 22.7 23.9
Very_much 912 52.0 54.6
Scantron_Error 5 0.3 0.3
NA 85 4.8 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 72 4.1 4.3
A_little_bit 63 3.6 3.8
Somewhat 192 10.9 11.5
Quite_a_bit 230 13.1 13.8
Very_much 1107 63.1 66.4
Scantron_Error 4 0.2 0.2
NA 86 4.9 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 99 5.6 6.0
A_little_bit 57 3.2 3.4
Somewhat 224 12.8 13.5
Quite_a_bit 212 12.1 12.8
Very_much 1064 60.7 64.1
Scantron_Error 3 0.2 0.2
NA 95 5.4 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 2 12.5 12.5
A_little_bit 0 0.0 0.0
Somewhat 1 6.2 6.2
Quite_a_bit 7 43.8 43.8
Very_much 6 37.5 37.5
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 1 6.2 6.2
A_little_bit 0 0.0 0.0
Somewhat 3 18.8 18.8
Quite_a_bit 4 25.0 25.0
Very_much 8 50.0 50.0
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 3 18.8 18.8
A_little_bit 2 12.5 12.5
Somewhat 5 31.2 31.2
Quite_a_bit 3 18.8 18.8
Very_much 3 18.8 18.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 2 12.5 13.3
A_little_bit 1 6.2 6.7
Somewhat 2 12.5 13.3
Quite_a_bit 3 18.8 20.0
Very_much 7 43.8 46.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 1 6.2 6.2
A_little_bit 0 0.0 0.0
Somewhat 1 6.2 6.2
Quite_a_bit 5 31.2 31.2
Very_much 9 56.2 56.2
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 1 6.2 6.2
A_little_bit 1 6.2 6.2
Somewhat 0 0.0 0.0
Quite_a_bit 7 43.8 43.8
Very_much 7 43.8 43.8
Scantron_Error 0 0.0 0.0
Total 16 100.0 100.0
rm(temp.dd) 

E12

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure

All data

  e12 <- as.factor(d[,"e12"])
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e12 <- ordered(e12, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 940 26.4 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 2617 73.6 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 74 23.1 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 247 76.9 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 45 21.4 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 165 78.6 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 84 26.7 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 231 73.3 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 112 31.5 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 244 68.5 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 157 26.8 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 428 73.2 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 463 26.4 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 1291 73.6 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 5 31.2 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 11 68.8 NA
Total 16 100.0 100
rm(temp.dd) 

E13

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure

All data

  e13 <- as.factor(d[,"e13"])
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88",
                     Scantron_Error="*")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 658 18.5 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 2899 81.5 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 75 23.4 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 246 76.6 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0 0
One 40 19 100
Two 0 0 0
Three 0 0 0
Four_more 0 0 0
Dont_know 0 0 0
Scantron_Error 0 0 0
NA 170 81 NA
Total 210 100 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 57 18.1 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 258 81.9 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 59 16.6 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 297 83.4 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 112 19.1 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 473 80.9 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0.0 0
One 311 17.7 100
Two 0 0.0 0
Three 0 0.0 0
Four_more 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 1443 82.3 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 0 0 0
One 4 25 100
Two 0 0 0
Three 0 0 0
Four_more 0 0 0
Dont_know 0 0 0
Scantron_Error 0 0 0
NA 12 75 NA
Total 16 100 100
rm(temp.dd) 

E14

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure

All data

  e14 <- as.factor(d[,"e14"])
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e14 <- ordered(e14, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 68 1.9 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 3489 98.1 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 5 1.6 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 316 98.4 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 2 1 100
Yes 0 0 0
Dont_know 0 0 0
Scantron_Error 0 0 0
NA 208 99 NA
Total 210 100 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 7 2.2 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 308 97.8 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 7 2 100
Yes 0 0 0
Dont_know 0 0 0
Scantron_Error 0 0 0
NA 349 98 NA
Total 356 100 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 14 2.4 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 571 97.6 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 31 1.8 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 1723 98.2 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 2 12.5 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 14 87.5 NA
Total 16 100.0 100
rm(temp.dd) 

E15

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure

All data

  e15 <- as.factor(d[,"e15"])
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e15 <- ordered(e15, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 147 4.1 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 3410 95.9 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 5 1.6 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 316 98.4 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 6 2.9 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 204 97.1 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 16 5.1 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 299 94.9 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 27 7.6 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 329 92.4 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 13 2.2 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 572 97.8 NA
Total 585 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 79 4.5 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 1675 95.5 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 1 6.2 100
Yes 0 0.0 0
Dont_know 0 0.0 0
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
rm(temp.dd) 

F1

  • F1. How tall are you?

All data

  f1cm <- d[,"f1cm"]
 
  
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
0 6 0.2 0.2
0 9 1 0.0 0.0
1 9 0.3 0.3
10 4 0.1 0.1
11 4 0.1 0.1
111 1 0.0 0.0
12 1 0.0 0.0
135 1 0.0 0.0
148 1 0.0 0.0
152 1 0.0 0.0
164 1 0.0 0.0
165 1 0.0 0.0
170 1 0.0 0.0
173 1 0.0 0.0
175 1 0.0 0.0
178 2 0.1 0.1
180 1 0.0 0.0
181 1 0.0 0.0
185 2 0.1 0.1
190 3 0.1 0.1
2 5 0.1 0.1
225 1 0.0 0.0
228 1 0.0 0.0
234 1 0.0 0.0
245 1 0.0 0.0
247 1 0.0 0.0
255 1 0.0 0.0
265 1 0.0 0.0
280 1 0.0 0.0
290 1 0.0 0.0
3 1 0.0 0.0
44 1 0.0 0.0
47 1 0.0 0.0
5 4 0.1 0.1
6 2 0.1 0.1
7 3 0.1 0.1
72 1 0.0 0.0
78 1 0.0 0.0
8 2 0.1 0.1
9 5 0.1 0.1
“NA” 3479 97.8 97.8
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
1 1 0.3 0.3
3 1 0.3 0.3
“NA” 319 99.4 99.4
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
185 1 0.5 0.5
8 1 0.5 0.5
“NA” 208 99.0 99.0
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
11 1 0.3 0.3
178 1 0.3 0.3
“NA” 313 99.4 99.4
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
111 1 0.3 0.3
148 1 0.3 0.3
2 1 0.3 0.3
225 1 0.3 0.3
5 1 0.3 0.3
7 1 0.3 0.3
9 1 0.3 0.3
“NA” 349 98.0 98.0
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
0 1 0.2 0.2
1 2 0.3 0.3
10 1 0.2 0.2
152 1 0.2 0.2
164 1 0.2 0.2
165 1 0.2 0.2
185 1 0.2 0.2
2 1 0.2 0.2
255 1 0.2 0.2
290 1 0.2 0.2
5 1 0.2 0.2
78 1 0.2 0.2
“NA” 572 97.8 97.8
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
0 5 0.3 0.3
0 9 1 0.1 0.1
1 6 0.3 0.3
10 3 0.2 0.2
11 3 0.2 0.2
12 1 0.1 0.1
135 1 0.1 0.1
170 1 0.1 0.1
173 1 0.1 0.1
175 1 0.1 0.1
178 1 0.1 0.1
180 1 0.1 0.1
181 1 0.1 0.1
190 3 0.2 0.2
2 3 0.2 0.2
228 1 0.1 0.1
234 1 0.1 0.1
245 1 0.1 0.1
247 1 0.1 0.1
265 1 0.1 0.1
280 1 0.1 0.1
44 1 0.1 0.1
47 1 0.1 0.1
5 2 0.1 0.1
6 2 0.1 0.1
7 2 0.1 0.1
72 1 0.1 0.1
8 1 0.1 0.1
9 4 0.2 0.2
“NA” 1702 97.0 97.0
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")
f1cm
n % val%
“NA” 16 100 100
Total 16 100 100
rm(temp.dd) 

F2

  • F2. How much do you current weight?

All data

  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
* 2 0.1 0.1
* 3 1 0.0 0.0
* 4 1 0.0 0.0
* 5 1 0.0 0.0
* 9 1 0.0 0.0
*0 2 0.1 0.1
*2 1 0.0 0.0
*35 1 0.0 0.0
*4 2 0.1 0.1
*5 2 0.1 0.1
*99 1 0.0 0.0
0 2 0.1 0.1
0* 1 0.0 0.0
1 8 0.2 0.2
1 * 3 0.1 0.1
1 8 1 0.0 0.0
1* 3 0.1 0.1
106 1 0.0 0.0
110 2 0.1 0.1
111 1 0.0 0.0
112 1 0.0 0.0
114 1 0.0 0.0
115 3 0.1 0.1
117 1 0.0 0.0
119 1 0.0 0.0
12 1 0.0 0.0
120 6 0.2 0.2
121 1 0.0 0.0
122 1 0.0 0.0
124 1 0.0 0.0
125 5 0.1 0.1
126 1 0.0 0.0
127 3 0.1 0.1
130 14 0.4 0.4
132 1 0.0 0.0
133 1 0.0 0.0
134 1 0.0 0.0
135 9 0.3 0.3
137 7 0.2 0.2
138 6 0.2 0.2
139 4 0.1 0.1
14* 1 0.0 0.0
140 20 0.6 0.6
141 3 0.1 0.1
142 5 0.1 0.1
143 2 0.1 0.1
144 1 0.0 0.0
145 15 0.4 0.4
146 4 0.1 0.1
147 8 0.2 0.2
148 8 0.2 0.2
149 2 0.1 0.1
150 40 1.1 1.1
151 1 0.0 0.0
152 8 0.2 0.2
153 7 0.2 0.2
154 10 0.3 0.3
155 35 1.0 1.0
156 11 0.3 0.3
157 8 0.2 0.2
158 15 0.4 0.4
159 7 0.2 0.2
160 57 1.6 1.6
161 7 0.2 0.2
162 16 0.4 0.4
163 12 0.3 0.3
164 10 0.3 0.3
165 50 1.4 1.4
166 6 0.2 0.2
167 21 0.6 0.6
168 24 0.7 0.7
169 12 0.3 0.3
170 71 2.0 2.0
171 9 0.3 0.3
172 18 0.5 0.5
173 18 0.5 0.5
174 15 0.4 0.4
175 68 1.9 1.9
176 17 0.5 0.5
177 13 0.4 0.4
178 31 0.9 0.9
179 8 0.2 0.2
18 2 0.1 0.1
180 84 2.4 2.4
181 7 0.2 0.2
182 26 0.7 0.7
183 20 0.6 0.6
184 18 0.5 0.5
185 85 2.4 2.4
186 16 0.4 0.4
187 23 0.6 0.6
188 31 0.9 0.9
189 28 0.8 0.8
190 98 2.8 2.8
191 9 0.3 0.3
192 24 0.7 0.7
193 21 0.6 0.6
194 12 0.3 0.3
195 66 1.9 1.9
196 17 0.5 0.5
197 25 0.7 0.7
198 38 1.1 1.1
199 18 0.5 0.5
2 10 0.3 0.3
2 * 1 0.0 0.0
2 1 1 0.0 0.0
2 2 1 0.0 0.0
2 6 1 0.0 0.0
2* 5 0.1 0.1
200 94 2.6 2.6
201 3 0.1 0.1
202 19 0.5 0.5
203 12 0.3 0.3
204 21 0.6 0.6
205 77 2.2 2.2
206 14 0.4 0.4
207 22 0.6 0.6
208 22 0.6 0.6
209 15 0.4 0.4
210 99 2.8 2.8
211 8 0.2 0.2
212 32 0.9 0.9
213 5 0.1 0.1
214 28 0.8 0.8
215 97 2.7 2.7
216 6 0.2 0.2
217 9 0.3 0.3
218 28 0.8 0.8
219 8 0.2 0.2
220 89 2.5 2.5
221 10 0.3 0.3
222 16 0.4 0.4
223 16 0.4 0.4
224 15 0.4 0.4
225 52 1.5 1.5
226 7 0.2 0.2
227 7 0.2 0.2
228 11 0.3 0.3
229 10 0.3 0.3
23 1 0.0 0.0
230 72 2.0 2.0
231 2 0.1 0.1
232 12 0.3 0.3
233 12 0.3 0.3
234 14 0.4 0.4
235 41 1.2 1.2
236 6 0.2 0.2
237 12 0.3 0.3
238 8 0.2 0.2
239 3 0.1 0.1
240 58 1.6 1.6
241 2 0.1 0.1
242 17 0.5 0.5
243 8 0.2 0.2
244 5 0.1 0.1
245 40 1.1 1.1
246 10 0.3 0.3
247 13 0.4 0.4
248 6 0.2 0.2
249 8 0.2 0.2
250 54 1.5 1.5
251 5 0.1 0.1
252 9 0.3 0.3
253 3 0.1 0.1
254 12 0.3 0.3
255 16 0.4 0.4
256 2 0.1 0.1
257 4 0.1 0.1
258 4 0.1 0.1
259 4 0.1 0.1
260 42 1.2 1.2
261 3 0.1 0.1
262 12 0.3 0.3
263 5 0.1 0.1
264 5 0.1 0.1
265 26 0.7 0.7
266 5 0.1 0.1
267 5 0.1 0.1
268 3 0.1 0.1
269 1 0.0 0.0
270 28 0.8 0.8
271 3 0.1 0.1
272 4 0.1 0.1
273 2 0.1 0.1
274 3 0.1 0.1
275 13 0.4 0.4
276 4 0.1 0.1
277 4 0.1 0.1
278 3 0.1 0.1
279 2 0.1 0.1
280 19 0.5 0.5
281 1 0.0 0.0
282 1 0.0 0.0
284 2 0.1 0.1
285 9 0.3 0.3
286 1 0.0 0.0
287 3 0.1 0.1
288 1 0.0 0.0
289 6 0.2 0.2
29 1 0.0 0.0
290 9 0.3 0.3
292 3 0.1 0.1
294 2 0.1 0.1
295 7 0.2 0.2
296 1 0.0 0.0
297 5 0.1 0.1
298 5 0.1 0.1
3 1 0.0 0.0
300 15 0.4 0.4
302 1 0.0 0.0
303 1 0.0 0.0
305 2 0.1 0.1
307 2 0.1 0.1
308 1 0.0 0.0
309 1 0.0 0.0
310 9 0.3 0.3
311 1 0.0 0.0
314 1 0.0 0.0
315 4 0.1 0.1
316 1 0.0 0.0
317 2 0.1 0.1
319 1 0.0 0.0
320 6 0.2 0.2
321 2 0.1 0.1
324 2 0.1 0.1
325 3 0.1 0.1
326 1 0.0 0.0
330 7 0.2 0.2
334 1 0.0 0.0
335 4 0.1 0.1
340 3 0.1 0.1
344 1 0.0 0.0
350 3 0.1 0.1
355 2 0.1 0.1
358 1 0.0 0.0
360 1 0.0 0.0
361 1 0.0 0.0
362 1 0.0 0.0
365 1 0.0 0.0
370 1 0.0 0.0
375 1 0.0 0.0
376 1 0.0 0.0
397 1 0.0 0.0
400 2 0.1 0.1
410 1 0.0 0.0
415 1 0.0 0.0
416 1 0.0 0.0
424 1 0.0 0.0
430 1 0.0 0.0
440 1 0.0 0.0
50 1 0.0 0.0
53 1 0.0 0.0
60 1 0.0 0.0
65 3 0.1 0.1
68 2 0.1 0.1
7 1 0.0 0.0
71 1 0.0 0.0
72 1 0.0 0.0
74 1 0.0 0.0
75 1 0.0 0.0
76 1 0.0 0.0
78 1 0.0 0.0
80 2 0.1 0.1
81 1 0.0 0.0
84 1 0.0 0.0
89 1 0.0 0.0
90 2 0.1 0.1
92 1 0.0 0.0
97 1 0.0 0.0
98 1 0.0 0.0
“NA” 386 10.9 10.9
Total 3557 100.0 100.0
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
* 1 0.0 0.0
0 10 0.3 0.3
1 2 0.1 0.1
1 7 1 0.0 0.0
10 1 0.0 0.0
110 1 0.0 0.0
111 2 0.1 0.1
113 1 0.0 0.0
137 1 0.0 0.0
175 1 0.0 0.0
2 4 0.1 0.1
22 1 0.0 0.0
3 1 0.0 0.0
37 1 0.0 0.0
45 1 0.0 0.0
5 1 0.0 0.0
50 3 0.1 0.1
55 2 0.1 0.1
6 1 0.0 0.0
60 1 0.0 0.0
61 1 0.0 0.0
64 1 0.0 0.0
65 1 0.0 0.0
7 1 0.0 0.0
75 1 0.0 0.0
76 1 0.0 0.0
8 1 0.0 0.0
82 1 0.0 0.0
85 1 0.0 0.0
86 1 0.0 0.0
88 1 0.0 0.0
9 2 0.1 0.1
90 1 0.0 0.0
91 1 0.0 0.0
92 1 0.0 0.0
“NA” 3504 98.5 98.5
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
*0 1 0.3 0.3
*99 1 0.3 0.3
115 1 0.3 0.3
120 1 0.3 0.3
125 1 0.3 0.3
130 1 0.3 0.3
137 1 0.3 0.3
138 1 0.3 0.3
140 2 0.6 0.6
145 2 0.6 0.6
148 1 0.3 0.3
150 4 1.2 1.2
152 1 0.3 0.3
155 2 0.6 0.6
158 2 0.6 0.6
160 10 3.1 3.1
163 2 0.6 0.6
164 1 0.3 0.3
165 2 0.6 0.6
166 1 0.3 0.3
167 3 0.9 0.9
168 3 0.9 0.9
169 1 0.3 0.3
170 7 2.2 2.2
172 2 0.6 0.6
173 4 1.2 1.2
175 6 1.9 1.9
176 2 0.6 0.6
177 2 0.6 0.6
178 4 1.2 1.2
180 7 2.2 2.2
181 1 0.3 0.3
182 1 0.3 0.3
183 3 0.9 0.9
184 2 0.6 0.6
185 6 1.9 1.9
186 3 0.9 0.9
187 2 0.6 0.6
188 2 0.6 0.6
189 1 0.3 0.3
190 7 2.2 2.2
191 2 0.6 0.6
192 3 0.9 0.9
193 1 0.3 0.3
195 4 1.2 1.2
196 1 0.3 0.3
197 1 0.3 0.3
198 3 0.9 0.9
199 1 0.3 0.3
2* 1 0.3 0.3
200 12 3.7 3.7
202 2 0.6 0.6
203 1 0.3 0.3
204 1 0.3 0.3
205 13 4.0 4.0
206 2 0.6 0.6
207 1 0.3 0.3
208 2 0.6 0.6
209 2 0.6 0.6
210 14 4.4 4.4
212 2 0.6 0.6
214 3 0.9 0.9
215 12 3.7 3.7
216 1 0.3 0.3
217 1 0.3 0.3
218 3 0.9 0.9
220 7 2.2 2.2
221 1 0.3 0.3
222 3 0.9 0.9
223 3 0.9 0.9
224 3 0.9 0.9
225 8 2.5 2.5
230 5 1.6 1.6
233 1 0.3 0.3
235 5 1.6 1.6
236 1 0.3 0.3
239 1 0.3 0.3
240 4 1.2 1.2
242 3 0.9 0.9
244 1 0.3 0.3
245 4 1.2 1.2
247 2 0.6 0.6
248 1 0.3 0.3
249 3 0.9 0.9
250 9 2.8 2.8
252 1 0.3 0.3
254 1 0.3 0.3
255 2 0.6 0.6
260 3 0.9 0.9
262 2 0.6 0.6
263 1 0.3 0.3
264 1 0.3 0.3
265 4 1.2 1.2
270 6 1.9 1.9
271 1 0.3 0.3
274 2 0.6 0.6
275 2 0.6 0.6
276 1 0.3 0.3
280 1 0.3 0.3
285 2 0.6 0.6
289 1 0.3 0.3
298 1 0.3 0.3
300 3 0.9 0.9
310 3 0.9 0.9
321 1 0.3 0.3
324 1 0.3 0.3
355 1 0.3 0.3
68 1 0.3 0.3
“NA” 18 5.6 5.6
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
* 1 0.3 0.3
137 1 0.3 0.3
“NA” 319 99.4 99.4
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
* 1 0.5 0.5
1 1 0.5 0.5
127 2 1.0 1.0
140 2 1.0 1.0
145 1 0.5 0.5
150 4 1.9 1.9
154 1 0.5 0.5
160 2 1.0 1.0
161 1 0.5 0.5
164 1 0.5 0.5
165 3 1.4 1.4
167 1 0.5 0.5
168 3 1.4 1.4
169 2 1.0 1.0
170 3 1.4 1.4
171 1 0.5 0.5
172 1 0.5 0.5
173 1 0.5 0.5
174 1 0.5 0.5
175 4 1.9 1.9
177 1 0.5 0.5
178 1 0.5 0.5
180 6 2.9 2.9
182 1 0.5 0.5
184 2 1.0 1.0
185 1 0.5 0.5
186 1 0.5 0.5
187 2 1.0 1.0
188 2 1.0 1.0
189 2 1.0 1.0
190 9 4.3 4.3
192 2 1.0 1.0
193 1 0.5 0.5
194 1 0.5 0.5
195 10 4.8 4.8
196 1 0.5 0.5
197 3 1.4 1.4
198 2 1.0 1.0
199 1 0.5 0.5
2* 2 1.0 1.0
200 5 2.4 2.4
204 1 0.5 0.5
205 4 1.9 1.9
207 1 0.5 0.5
210 4 1.9 1.9
211 1 0.5 0.5
212 3 1.4 1.4
214 2 1.0 1.0
215 1 0.5 0.5
216 1 0.5 0.5
220 11 5.2 5.2
221 1 0.5 0.5
224 3 1.4 1.4
225 3 1.4 1.4
226 1 0.5 0.5
228 1 0.5 0.5
229 3 1.4 1.4
23 1 0.5 0.5
230 5 2.4 2.4
233 1 0.5 0.5
235 2 1.0 1.0
237 2 1.0 1.0
240 3 1.4 1.4
243 2 1.0 1.0
245 1 0.5 0.5
247 1 0.5 0.5
249 2 1.0 1.0
250 1 0.5 0.5
254 1 0.5 0.5
255 3 1.4 1.4
260 3 1.4 1.4
262 1 0.5 0.5
265 1 0.5 0.5
266 1 0.5 0.5
267 1 0.5 0.5
268 1 0.5 0.5
270 4 1.9 1.9
272 1 0.5 0.5
280 1 0.5 0.5
285 1 0.5 0.5
292 1 0.5 0.5
310 1 0.5 0.5
311 1 0.5 0.5
315 1 0.5 0.5
330 1 0.5 0.5
340 1 0.5 0.5
350 1 0.5 0.5
416 1 0.5 0.5
53 1 0.5 0.5
65 1 0.5 0.5
75 1 0.5 0.5
90 1 0.5 0.5
“NA” 25 11.9 11.9
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
111 1 0.5 0.5
8 1 0.5 0.5
88 1 0.5 0.5
92 1 0.5 0.5
“NA” 206 98.1 98.1
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
*5 1 0.3 0.3
112 1 0.3 0.3
114 1 0.3 0.3
125 1 0.3 0.3
138 1 0.3 0.3
140 1 0.3 0.3
145 2 0.6 0.6
146 1 0.3 0.3
148 1 0.3 0.3
150 4 1.3 1.3
152 1 0.3 0.3
153 1 0.3 0.3
154 1 0.3 0.3
155 3 1.0 1.0
156 1 0.3 0.3
157 2 0.6 0.6
158 1 0.3 0.3
159 1 0.3 0.3
160 4 1.3 1.3
162 3 1.0 1.0
163 1 0.3 0.3
165 6 1.9 1.9
167 5 1.6 1.6
168 1 0.3 0.3
170 5 1.6 1.6
172 2 0.6 0.6
173 2 0.6 0.6
174 1 0.3 0.3
175 7 2.2 2.2
176 1 0.3 0.3
177 3 1.0 1.0
178 5 1.6 1.6
179 1 0.3 0.3
180 10 3.2 3.2
182 5 1.6 1.6
184 4 1.3 1.3
185 10 3.2 3.2
187 2 0.6 0.6
188 1 0.3 0.3
189 5 1.6 1.6
190 8 2.5 2.5
192 2 0.6 0.6
193 2 0.6 0.6
195 9 2.9 2.9
196 2 0.6 0.6
197 1 0.3 0.3
198 1 0.3 0.3
199 3 1.0 1.0
2 1 0.3 0.3
2 * 1 0.3 0.3
2 2 1 0.3 0.3
200 10 3.2 3.2
202 3 1.0 1.0
205 6 1.9 1.9
206 1 0.3 0.3
207 3 1.0 1.0
208 5 1.6 1.6
209 3 1.0 1.0
210 10 3.2 3.2
211 1 0.3 0.3
212 1 0.3 0.3
214 2 0.6 0.6
215 9 2.9 2.9
219 2 0.6 0.6
220 9 2.9 2.9
222 2 0.6 0.6
223 1 0.3 0.3
225 1 0.3 0.3
226 3 1.0 1.0
228 1 0.3 0.3
230 3 1.0 1.0
232 2 0.6 0.6
233 1 0.3 0.3
234 1 0.3 0.3
235 3 1.0 1.0
237 1 0.3 0.3
238 2 0.6 0.6
239 1 0.3 0.3
240 6 1.9 1.9
242 2 0.6 0.6
243 1 0.3 0.3
244 1 0.3 0.3
245 4 1.3 1.3
249 1 0.3 0.3
250 3 1.0 1.0
251 1 0.3 0.3
252 1 0.3 0.3
254 2 0.6 0.6
260 2 0.6 0.6
261 1 0.3 0.3
262 2 0.6 0.6
263 1 0.3 0.3
265 6 1.9 1.9
270 1 0.3 0.3
273 1 0.3 0.3
275 3 1.0 1.0
280 2 0.6 0.6
285 1 0.3 0.3
287 1 0.3 0.3
289 1 0.3 0.3
290 2 0.6 0.6
294 1 0.3 0.3
300 1 0.3 0.3
305 1 0.3 0.3
308 1 0.3 0.3
325 1 0.3 0.3
355 1 0.3 0.3
440 1 0.3 0.3
65 1 0.3 0.3
81 1 0.3 0.3
“NA” 33 10.5 10.5
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
0 2 0.6 0.6
2 1 0.3 0.3
75 1 0.3 0.3
“NA” 311 98.7 98.7
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
*0 1 0.3 0.3
*2 1 0.3 0.3
*4 1 0.3 0.3
0* 1 0.3 0.3
1 2 0.6 0.6
1* 1 0.3 0.3
115 2 0.6 0.6
120 1 0.3 0.3
130 4 1.1 1.1
135 2 0.6 0.6
137 2 0.6 0.6
138 1 0.3 0.3
139 1 0.3 0.3
140 3 0.8 0.8
141 1 0.3 0.3
143 1 0.3 0.3
145 2 0.6 0.6
146 1 0.3 0.3
147 2 0.6 0.6
150 1 0.3 0.3
152 1 0.3 0.3
153 3 0.8 0.8
154 1 0.3 0.3
155 6 1.7 1.7
156 1 0.3 0.3
157 2 0.6 0.6
158 2 0.6 0.6
159 1 0.3 0.3
160 6 1.7 1.7
161 1 0.3 0.3
162 1 0.3 0.3
163 1 0.3 0.3
164 1 0.3 0.3
165 3 0.8 0.8
166 2 0.6 0.6
168 2 0.6 0.6
169 1 0.3 0.3
170 7 2.0 2.0
175 6 1.7 1.7
176 1 0.3 0.3
177 2 0.6 0.6
178 3 0.8 0.8
180 6 1.7 1.7
182 4 1.1 1.1
183 5 1.4 1.4
184 1 0.3 0.3
185 15 4.2 4.2
186 2 0.6 0.6
187 1 0.3 0.3
188 1 0.3 0.3
189 3 0.8 0.8
190 9 2.5 2.5
191 1 0.3 0.3
192 1 0.3 0.3
193 3 0.8 0.8
194 2 0.6 0.6
195 4 1.1 1.1
196 1 0.3 0.3
197 3 0.8 0.8
198 2 0.6 0.6
199 1 0.3 0.3
2 1 0.3 0.3
200 7 2.0 2.0
201 1 0.3 0.3
203 3 0.8 0.8
205 3 0.8 0.8
206 2 0.6 0.6
207 3 0.8 0.8
208 4 1.1 1.1
209 1 0.3 0.3
210 8 2.2 2.2
211 1 0.3 0.3
212 4 1.1 1.1
213 1 0.3 0.3
214 1 0.3 0.3
215 13 3.7 3.7
216 1 0.3 0.3
218 4 1.1 1.1
220 11 3.1 3.1
221 1 0.3 0.3
222 1 0.3 0.3
223 1 0.3 0.3
224 1 0.3 0.3
225 3 0.8 0.8
230 14 3.9 3.9
232 2 0.6 0.6
233 2 0.6 0.6
234 2 0.6 0.6
235 4 1.1 1.1
237 2 0.6 0.6
240 6 1.7 1.7
241 1 0.3 0.3
242 1 0.3 0.3
243 1 0.3 0.3
244 1 0.3 0.3
245 4 1.1 1.1
246 1 0.3 0.3
247 2 0.6 0.6
250 4 1.1 1.1
255 3 0.8 0.8
257 1 0.3 0.3
260 8 2.2 2.2
261 1 0.3 0.3
262 2 0.6 0.6
264 1 0.3 0.3
265 2 0.6 0.6
266 2 0.6 0.6
268 1 0.3 0.3
270 2 0.6 0.6
271 1 0.3 0.3
275 3 0.8 0.8
277 1 0.3 0.3
280 3 0.8 0.8
281 1 0.3 0.3
285 1 0.3 0.3
287 1 0.3 0.3
295 2 0.6 0.6
298 1 0.3 0.3
315 1 0.3 0.3
317 1 0.3 0.3
350 1 0.3 0.3
361 1 0.3 0.3
375 1 0.3 0.3
376 1 0.3 0.3
397 1 0.3 0.3
424 1 0.3 0.3
68 1 0.3 0.3
72 1 0.3 0.3
74 1 0.3 0.3
76 1 0.3 0.3
“NA” 34 9.6 9.6
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
10 1 0.3 0.3
2 1 0.3 0.3
55 1 0.3 0.3
61 1 0.3 0.3
“NA” 352 98.9 98.9
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
*5 1 0.2 0.2
1 2 0.3 0.3
1* 1 0.2 0.2
111 1 0.2 0.2
12 1 0.2 0.2
120 2 0.3 0.3
124 1 0.2 0.2
126 1 0.2 0.2
127 1 0.2 0.2
130 5 0.9 0.9
132 1 0.2 0.2
135 1 0.2 0.2
137 1 0.2 0.2
138 2 0.3 0.3
139 1 0.2 0.2
140 2 0.3 0.3
141 1 0.2 0.2
145 4 0.7 0.7
147 2 0.3 0.3
148 3 0.5 0.5
149 2 0.3 0.3
150 9 1.5 1.5
151 1 0.2 0.2
152 2 0.3 0.3
153 1 0.2 0.2
155 6 1.0 1.0
156 3 0.5 0.5
157 2 0.3 0.3
158 1 0.2 0.2
160 10 1.7 1.7
161 1 0.2 0.2
162 2 0.3 0.3
163 2 0.3 0.3
164 2 0.3 0.3
165 9 1.5 1.5
167 5 0.9 0.9
168 2 0.3 0.3
169 2 0.3 0.3
170 9 1.5 1.5
173 3 0.5 0.5
174 6 1.0 1.0
175 13 2.2 2.2
176 2 0.3 0.3
177 1 0.2 0.2
178 4 0.7 0.7
179 1 0.2 0.2
180 24 4.1 4.1
181 1 0.2 0.2
182 2 0.3 0.3
183 5 0.9 0.9
184 3 0.5 0.5
185 17 2.9 2.9
186 2 0.3 0.3
187 4 0.7 0.7
188 6 1.0 1.0
189 3 0.5 0.5
190 14 2.4 2.4
191 1 0.2 0.2
192 3 0.5 0.5
193 3 0.5 0.5
194 2 0.3 0.3
195 11 1.9 1.9
196 4 0.7 0.7
197 4 0.7 0.7
198 11 1.9 1.9
199 4 0.7 0.7
2 2 0.3 0.3
200 10 1.7 1.7
202 7 1.2 1.2
203 3 0.5 0.5
204 6 1.0 1.0
205 17 2.9 2.9
206 4 0.7 0.7
207 4 0.7 0.7
208 2 0.3 0.3
210 16 2.7 2.7
211 2 0.3 0.3
212 7 1.2 1.2
214 8 1.4 1.4
215 9 1.5 1.5
216 1 0.2 0.2
217 2 0.3 0.3
218 5 0.9 0.9
219 1 0.2 0.2
220 10 1.7 1.7
221 2 0.3 0.3
222 2 0.3 0.3
224 3 0.5 0.5
225 10 1.7 1.7
226 1 0.2 0.2
227 2 0.3 0.3
228 2 0.3 0.3
229 2 0.3 0.3
230 15 2.6 2.6
232 1 0.2 0.2
233 5 0.9 0.9
234 5 0.9 0.9
235 2 0.3 0.3
236 2 0.3 0.3
237 2 0.3 0.3
238 3 0.5 0.5
240 12 2.1 2.1
242 2 0.3 0.3
245 5 0.9 0.9
246 3 0.5 0.5
247 1 0.2 0.2
248 1 0.2 0.2
250 5 0.9 0.9
251 2 0.3 0.3
252 1 0.2 0.2
254 3 0.5 0.5
255 2 0.3 0.3
258 2 0.3 0.3
259 2 0.3 0.3
260 7 1.2 1.2
262 2 0.3 0.3
265 1 0.2 0.2
267 1 0.2 0.2
269 1 0.2 0.2
270 3 0.5 0.5
271 1 0.2 0.2
273 1 0.2 0.2
275 3 0.5 0.5
276 1 0.2 0.2
277 1 0.2 0.2
280 1 0.2 0.2
285 2 0.3 0.3
288 1 0.2 0.2
290 4 0.7 0.7
292 1 0.2 0.2
295 1 0.2 0.2
296 1 0.2 0.2
300 3 0.5 0.5
303 1 0.2 0.2
307 1 0.2 0.2
310 2 0.3 0.3
316 1 0.2 0.2
320 3 0.5 0.5
326 1 0.2 0.2
330 2 0.3 0.3
334 1 0.2 0.2
335 2 0.3 0.3
340 1 0.2 0.2
350 1 0.2 0.2
362 1 0.2 0.2
400 1 0.2 0.2
415 1 0.2 0.2
430 1 0.2 0.2
89 1 0.2 0.2
90 1 0.2 0.2
“NA” 52 8.9 8.9
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
0 1 0.2 0.2
50 1 0.2 0.2
55 1 0.2 0.2
6 1 0.2 0.2
60 1 0.2 0.2
64 1 0.2 0.2
85 1 0.2 0.2
86 1 0.2 0.2
9 2 0.3 0.3
“NA” 575 98.3 98.3
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
* 1 0.1 0.1
* 3 1 0.1 0.1
* 4 1 0.1 0.1
* 5 1 0.1 0.1
* 9 1 0.1 0.1
*35 1 0.1 0.1
*4 1 0.1 0.1
0 2 0.1 0.1
1 3 0.2 0.2
1 * 3 0.2 0.2
1 8 1 0.1 0.1
1* 1 0.1 0.1
106 1 0.1 0.1
110 2 0.1 0.1
117 1 0.1 0.1
119 1 0.1 0.1
120 2 0.1 0.1
121 1 0.1 0.1
122 1 0.1 0.1
125 3 0.2 0.2
130 4 0.2 0.2
133 1 0.1 0.1
134 1 0.1 0.1
135 6 0.3 0.3
137 3 0.2 0.2
138 1 0.1 0.1
139 2 0.1 0.1
14* 1 0.1 0.1
140 9 0.5 0.5
141 1 0.1 0.1
142 5 0.3 0.3
143 1 0.1 0.1
144 1 0.1 0.1
145 4 0.2 0.2
146 2 0.1 0.1
147 4 0.2 0.2
148 3 0.2 0.2
150 18 1.0 1.0
152 3 0.2 0.2
153 2 0.1 0.1
154 7 0.4 0.4
155 18 1.0 1.0
156 6 0.3 0.3
157 2 0.1 0.1
158 9 0.5 0.5
159 5 0.3 0.3
160 25 1.4 1.4
161 4 0.2 0.2
162 10 0.6 0.6
163 6 0.3 0.3
164 5 0.3 0.3
165 27 1.5 1.5
166 3 0.2 0.2
167 7 0.4 0.4
168 13 0.7 0.7
169 6 0.3 0.3
170 40 2.3 2.3
171 8 0.5 0.5
172 13 0.7 0.7
173 8 0.5 0.5
174 7 0.4 0.4
175 32 1.8 1.8
176 11 0.6 0.6
177 4 0.2 0.2
178 14 0.8 0.8
179 5 0.3 0.3
18 2 0.1 0.1
180 31 1.8 1.8
181 5 0.3 0.3
182 13 0.7 0.7
183 7 0.4 0.4
184 6 0.3 0.3
185 34 1.9 1.9
186 8 0.5 0.5
187 11 0.6 0.6
188 18 1.0 1.0
189 13 0.7 0.7
190 50 2.9 2.9
191 5 0.3 0.3
192 13 0.7 0.7
193 11 0.6 0.6
194 7 0.4 0.4
195 28 1.6 1.6
196 8 0.5 0.5
197 13 0.7 0.7
198 19 1.1 1.1
199 8 0.5 0.5
2 6 0.3 0.3
2 1 1 0.1 0.1
2 6 1 0.1 0.1
2* 2 0.1 0.1
200 49 2.8 2.8
201 2 0.1 0.1
202 7 0.4 0.4
203 5 0.3 0.3
204 13 0.7 0.7
205 34 1.9 1.9
206 5 0.3 0.3
207 9 0.5 0.5
208 9 0.5 0.5
209 9 0.5 0.5
210 46 2.6 2.6
211 3 0.2 0.2
212 15 0.9 0.9
213 4 0.2 0.2
214 12 0.7 0.7
215 53 3.0 3.0
216 2 0.1 0.1
217 6 0.3 0.3
218 16 0.9 0.9
219 5 0.3 0.3
220 41 2.3 2.3
221 5 0.3 0.3
222 8 0.5 0.5
223 11 0.6 0.6
224 5 0.3 0.3
225 27 1.5 1.5
226 2 0.1 0.1
227 5 0.3 0.3
228 7 0.4 0.4
229 5 0.3 0.3
230 30 1.7 1.7
231 2 0.1 0.1
232 7 0.4 0.4
233 2 0.1 0.1
234 6 0.3 0.3
235 25 1.4 1.4
236 3 0.2 0.2
237 5 0.3 0.3
238 3 0.2 0.2
239 1 0.1 0.1
240 27 1.5 1.5
241 1 0.1 0.1
242 8 0.5 0.5
243 3 0.2 0.2
244 2 0.1 0.1
245 22 1.3 1.3
246 6 0.3 0.3
247 7 0.4 0.4
248 4 0.2 0.2
249 2 0.1 0.1
250 32 1.8 1.8
251 2 0.1 0.1
252 6 0.3 0.3
253 3 0.2 0.2
254 5 0.3 0.3
255 6 0.3 0.3
256 2 0.1 0.1
257 3 0.2 0.2
258 2 0.1 0.1
259 1 0.1 0.1
260 19 1.1 1.1
261 1 0.1 0.1
262 3 0.2 0.2
263 3 0.2 0.2
264 3 0.2 0.2
265 12 0.7 0.7
266 2 0.1 0.1
267 3 0.2 0.2
268 1 0.1 0.1
270 12 0.7 0.7
272 3 0.2 0.2
274 1 0.1 0.1
275 1 0.1 0.1
276 2 0.1 0.1
277 2 0.1 0.1
278 3 0.2 0.2
279 2 0.1 0.1
280 11 0.6 0.6
282 1 0.1 0.1
284 2 0.1 0.1
285 2 0.1 0.1
286 1 0.1 0.1
287 1 0.1 0.1
289 4 0.2 0.2
29 1 0.1 0.1
290 3 0.2 0.2
292 1 0.1 0.1
294 1 0.1 0.1
295 4 0.2 0.2
297 5 0.3 0.3
298 3 0.2 0.2
3 1 0.1 0.1
300 8 0.5 0.5
302 1 0.1 0.1
305 1 0.1 0.1
307 1 0.1 0.1
309 1 0.1 0.1
310 3 0.2 0.2
314 1 0.1 0.1
315 2 0.1 0.1
317 1 0.1 0.1
319 1 0.1 0.1
320 3 0.2 0.2
321 1 0.1 0.1
324 1 0.1 0.1
325 2 0.1 0.1
330 4 0.2 0.2
335 2 0.1 0.1
340 1 0.1 0.1
344 1 0.1 0.1
358 1 0.1 0.1
360 1 0.1 0.1
365 1 0.1 0.1
370 1 0.1 0.1
400 1 0.1 0.1
410 1 0.1 0.1
50 1 0.1 0.1
60 1 0.1 0.1
65 1 0.1 0.1
7 1 0.1 0.1
71 1 0.1 0.1
78 1 0.1 0.1
80 2 0.1 0.1
84 1 0.1 0.1
92 1 0.1 0.1
97 1 0.1 0.1
98 1 0.1 0.1
“NA” 223 12.7 12.7
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
0 7 0.4 0.4
1 2 0.1 0.1
1 7 1 0.1 0.1
110 1 0.1 0.1
111 1 0.1 0.1
113 1 0.1 0.1
175 1 0.1 0.1
2 2 0.1 0.1
22 1 0.1 0.1
3 1 0.1 0.1
37 1 0.1 0.1
45 1 0.1 0.1
5 1 0.1 0.1
50 2 0.1 0.1
65 1 0.1 0.1
7 1 0.1 0.1
76 1 0.1 0.1
82 1 0.1 0.1
90 1 0.1 0.1
91 1 0.1 0.1
“NA” 1725 98.3 98.3
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")
f2lbs
n % val%
140 1 6.2 6.2
179 1 6.2 6.2
185 2 12.5 12.5
187 1 6.2 6.2
188 1 6.2 6.2
189 1 6.2 6.2
190 1 6.2 6.2
200 1 6.2 6.2
207 1 6.2 6.2
210 1 6.2 6.2
242 1 6.2 6.2
243 1 6.2 6.2
259 1 6.2 6.2
275 1 6.2 6.2
“NA” 1 6.2 6.2
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
“NA” 16 100 100
Total 16 100 100
rm(temp.dd) 

F3

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise

All data

  f3 <- as.factor(d[,"f3"])
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1",
                     Scantron_Error="*")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1","Scantron_Error"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 580 16.3 17.6 16.3 17.6
Per_week_3_4 960 27.0 29.1 43.3 46.7
Per_week_1_2 934 26.3 28.3 69.6 75.0
Per_week_less_1 823 23.1 24.9 92.7 99.9
Scantron_Error 3 0.1 0.1 92.8 100.0
NA 257 7.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 88 27.4 27.8 27.4 27.8
Per_week_3_4 96 29.9 30.4 57.3 58.2
Per_week_1_2 80 24.9 25.3 82.2 83.5
Per_week_less_1 52 16.2 16.5 98.4 100.0
Scantron_Error 0 0.0 0.0 98.4 100.0
NA 5 1.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 39 18.6 19.6 18.6 19.6
Per_week_3_4 75 35.7 37.7 54.3 57.3
Per_week_1_2 54 25.7 27.1 80.0 84.4
Per_week_less_1 31 14.8 15.6 94.8 100.0
Scantron_Error 0 0.0 0.0 94.8 100.0
NA 11 5.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 60 19.0 20.8 19.0 20.8
Per_week_3_4 92 29.2 31.9 48.3 52.8
Per_week_1_2 83 26.3 28.8 74.6 81.6
Per_week_less_1 53 16.8 18.4 91.4 100.0
Scantron_Error 0 0.0 0.0 91.4 100.0
NA 27 8.6 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 47 13.2 14.4 13.2 14.4
Per_week_3_4 78 21.9 23.9 35.1 38.3
Per_week_1_2 100 28.1 30.7 63.2 69.0
Per_week_less_1 101 28.4 31.0 91.6 100.0
Scantron_Error 0 0.0 0.0 91.6 100.0
NA 30 8.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 84 14.4 15.6 14.4 15.6
Per_week_3_4 141 24.1 26.2 38.5 41.8
Per_week_1_2 154 26.3 28.6 64.8 70.4
Per_week_less_1 159 27.2 29.6 92.0 100.0
Scantron_Error 0 0.0 0.0 92.0 100.0
NA 47 8.0 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 260 14.8 16.0 14.8 16.0
Per_week_3_4 476 27.1 29.4 42.0 45.4
Per_week_1_2 457 26.1 28.2 68.0 73.6
Per_week_less_1 425 24.2 26.2 92.2 99.8
Scantron_Error 3 0.2 0.2 92.4 100.0
NA 133 7.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 2 12.5 16.7 12.5 16.7
Per_week_3_4 2 12.5 16.7 25.0 33.3
Per_week_1_2 6 37.5 50.0 62.5 83.3
Per_week_less_1 2 12.5 16.7 75.0 100.0
Scantron_Error 0 0.0 0.0 75.0 100.0
NA 4 25.0 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
rm(temp.dd) 

F4

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise

All data

  f4 <- as.factor(d[,"f4"])
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1",
                     Scantron_Error="*")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise","Scantron_Error"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 589 16.6 17.9 16.6 17.9
Between_30_min_1_hour 1363 38.3 41.4 54.9 59.3
More_than_1_hour 734 20.6 22.3 75.5 81.6
Do_not_exercise 603 17.0 18.3 92.5 99.9
Scantron_Error 4 0.1 0.1 92.6 100.0
NA 264 7.4 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 37 11.5 11.8 11.5 11.8
Between_30_min_1_hour 158 49.2 50.3 60.7 62.1
More_than_1_hour 83 25.9 26.4 86.6 88.5
Do_not_exercise 35 10.9 11.1 97.5 99.7
Scantron_Error 1 0.3 0.3 97.8 100.0
NA 7 2.2 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 33 15.7 16.8 15.7 16.8
Between_30_min_1_hour 90 42.9 45.7 58.6 62.4
More_than_1_hour 61 29.0 31.0 87.6 93.4
Do_not_exercise 13 6.2 6.6 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 13 6.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 51 16.2 17.8 16.2 17.8
Between_30_min_1_hour 122 38.7 42.5 54.9 60.3
More_than_1_hour 78 24.8 27.2 79.7 87.5
Do_not_exercise 36 11.4 12.5 91.1 100.0
Scantron_Error 0 0.0 0.0 91.1 100.0
NA 28 8.9 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 84 23.6 25.8 23.6 25.8
Between_30_min_1_hour 118 33.1 36.3 56.7 62.2
More_than_1_hour 62 17.4 19.1 74.2 81.2
Do_not_exercise 61 17.1 18.8 91.3 100.0
Scantron_Error 0 0.0 0.0 91.3 100.0
NA 31 8.7 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 97 16.6 18.1 16.6 18.1
Between_30_min_1_hour 197 33.7 36.7 50.3 54.7
More_than_1_hour 111 19.0 20.7 69.2 75.4
Do_not_exercise 131 22.4 24.4 91.6 99.8
Scantron_Error 1 0.2 0.2 91.8 100.0
NA 48 8.2 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 286 16.3 17.6 16.3 17.6
Between_30_min_1_hour 669 38.1 41.3 54.4 58.9
More_than_1_hour 338 19.3 20.9 73.7 79.8
Do_not_exercise 326 18.6 20.1 92.3 99.9
Scantron_Error 2 0.1 0.1 92.4 100.0
NA 133 7.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")
f4
n % val% %cum val%cum
Less_than_30_min 1 6.2 8.3 6.2 8.3
Between_30_min_1_hour 9 56.2 75.0 62.5 83.3
More_than_1_hour 1 6.2 8.3 68.8 91.7
Do_not_exercise 1 6.2 8.3 75.0 100.0
Scantron_Error 0 0.0 0.0 75.0 100.0
NA 4 25.0 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
rm(temp.dd) 

F5

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink

All data

  f5 <- as.factor(d[,"f5"])
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1",
                     Scantron_Error="*")
  f5 <- ordered(f5, c("Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink","Scantron_Error"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 135 3.8 4.1 3.8 4.1
Per_week_3_4_times 390 11.0 11.8 14.8 15.9
Per_week_1_2_times 518 14.6 15.7 29.3 31.6
Per_week_fewer_once 668 18.8 20.2 48.1 51.8
Not_drink 1588 44.6 48.0 92.7 99.8
Scantron_Error 6 0.2 0.2 92.9 100.0
NA 252 7.1 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 16 5.0 5.2 5.0 5.2
Per_week_3_4_times 40 12.5 12.9 17.4 18.1
Per_week_1_2_times 42 13.1 13.5 30.5 31.6
Per_week_fewer_once 65 20.2 21.0 50.8 52.6
Not_drink 147 45.8 47.4 96.6 100.0
Scantron_Error 0 0.0 0.0 96.6 100.0
NA 11 3.4 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 14 6.7 7.3 6.7 7.3
Per_week_3_4_times 22 10.5 11.5 17.1 18.8
Per_week_1_2_times 37 17.6 19.4 34.8 38.2
Per_week_fewer_once 33 15.7 17.3 50.5 55.5
Not_drink 85 40.5 44.5 91.0 100.0
Scantron_Error 0 0.0 0.0 91.0 100.0
NA 19 9.0 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 13 4.1 4.5 4.1 4.5
Per_week_3_4_times 44 14.0 15.2 18.1 19.7
Per_week_1_2_times 36 11.4 12.4 29.5 32.1
Per_week_fewer_once 60 19.0 20.7 48.6 52.8
Not_drink 136 43.2 46.9 91.7 99.7
Scantron_Error 1 0.3 0.3 92.1 100.0
NA 25 7.9 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 10 2.8 3.0 2.8 3.0
Per_week_3_4_times 42 11.8 12.6 14.6 15.6
Per_week_1_2_times 65 18.3 19.5 32.9 35.1
Per_week_fewer_once 63 17.7 18.9 50.6 54.1
Not_drink 151 42.4 45.3 93.0 99.4
Scantron_Error 2 0.6 0.6 93.5 100.0
NA 23 6.5 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 25 4.3 4.6 4.3 4.6
Per_week_3_4_times 65 11.1 12.0 15.4 16.6
Per_week_1_2_times 90 15.4 16.6 30.8 33.3
Per_week_fewer_once 99 16.9 18.3 47.7 51.6
Not_drink 261 44.6 48.2 92.3 99.8
Scantron_Error 1 0.2 0.2 92.5 100.0
NA 44 7.5 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 57 3.2 3.5 3.2 3.5
Per_week_3_4_times 174 9.9 10.7 13.2 14.2
Per_week_1_2_times 247 14.1 15.2 27.3 29.4
Per_week_fewer_once 347 19.8 21.4 47.0 50.8
Not_drink 798 45.5 49.1 92.5 99.9
Scantron_Error 2 0.1 0.1 92.6 100.0
NA 129 7.4 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Per_week_5_6_times 0 0.0 0.0 0.0 0.0
Per_week_3_4_times 3 18.8 20.0 18.8 20.0
Per_week_1_2_times 1 6.2 6.7 25.0 26.7
Per_week_fewer_once 1 6.2 6.7 31.2 33.3
Not_drink 10 62.5 66.7 93.8 100.0
Scantron_Error 0 0.0 0.0 93.8 100.0
NA 1 6.2 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
rm(temp.dd) 

F6

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink

All data

  f6 <- as.factor(d[,"f6"])
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1",
                     Scantron_Error="*")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink","Scantron_Error"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 332 9.3 10.5 9.3 10.5
One_to_two_drinks 1467 41.2 46.3 50.6 56.8
Not_drink 1368 38.5 43.2 89.0 100.0
Scantron_Error 1 0.0 0.0 89.1 100.0
NA 389 10.9 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 22 6.9 7.0 6.9 7.0
One_to_two_drinks 141 43.9 44.9 50.8 51.9
Not_drink 151 47.0 48.1 97.8 100.0
Scantron_Error 0 0.0 0.0 97.8 100.0
NA 7 2.2 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 17 8.1 9.1 8.1 9.1
One_to_two_drinks 103 49.0 55.4 57.1 64.5
Not_drink 66 31.4 35.5 88.6 100.0
Scantron_Error 0 0.0 0.0 88.6 100.0
NA 24 11.4 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 36 11.4 12.6 11.4 12.6
One_to_two_drinks 132 41.9 46.3 53.3 58.9
Not_drink 117 37.1 41.1 90.5 100.0
Scantron_Error 0 0.0 0.0 90.5 100.0
NA 30 9.5 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 32 9.0 10.4 9.0 10.4
One_to_two_drinks 151 42.4 49.2 51.4 59.6
Not_drink 124 34.8 40.4 86.2 100.0
Scantron_Error 0 0.0 0.0 86.2 100.0
NA 49 13.8 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 74 12.6 13.5 12.6 13.5
One_to_two_drinks 225 38.5 41.1 51.1 54.7
Not_drink 248 42.4 45.3 93.5 100.0
Scantron_Error 0 0.0 0.0 93.5 100.0
NA 38 6.5 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 150 8.6 9.9 8.6 9.9
One_to_two_drinks 711 40.5 46.9 49.1 56.8
Not_drink 654 37.3 43.1 86.4 99.9
Scantron_Error 1 0.1 0.1 86.4 100.0
NA 238 13.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 1 6.2 7.7 6.2 7.7
One_to_two_drinks 4 25.0 30.8 31.2 38.5
Not_drink 8 50.0 61.5 81.2 100.0
Scantron_Error 0 0.0 0.0 81.2 100.0
NA 3 18.8 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
rm(temp.dd) 

F7

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”

All data

  f7 <- as.factor(d[,"f7"])
  levels(f7) <- list(Yes="2",
                     No="1",
                     Scantron_Error="*")
  f7 <- ordered(f7, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 1806 50.8 53.6 50.8 53.6
Yes 1565 44.0 46.4 94.8 100.0
Scantron_Error 0 0.0 0.0 94.8 100.0
NA 186 5.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 5 0.1 0.1
1 2 0.1 0.1
10 12 0.3 0.3
11 6 0.2 0.2
12 25 0.7 0.7
13 40 1.1 1.1
14 47 1.3 1.3
15 111 3.1 3.1
16 130 3.7 3.7
17 127 3.6 3.6
18 198 5.6 5.6
19 107 3.0 3.0
20 111 3.1 3.1
21 61 1.7 1.7
22 47 1.3 1.3
23 29 0.8 0.8
24 15 0.4 0.4
25 42 1.2 1.2
26 9 0.3 0.3
27 13 0.4 0.4
28 14 0.4 0.4
29 2 0.1 0.1
3 1 0.0 0.0
30 30 0.8 0.8
31 2 0.1 0.1
32 6 0.2 0.2
33 3 0.1 0.1
34 5 0.1 0.1
35 18 0.5 0.5
36 1 0.0 0.0
38 2 0.1 0.1
4 1 0.0 0.0
40 7 0.2 0.2
41 1 0.0 0.0
42 1 0.0 0.0
44 1 0.0 0.0
45 3 0.1 0.1
48 1 0.0 0.0
49 3 0.1 0.1
5 1 0.0 0.0
50 3 0.1 0.1
54 2 0.1 0.1
55 1 0.0 0.0
56 1 0.0 0.0
59 1 0.0 0.0
60 1 0.0 0.0
62 1 0.0 0.0
7 2 0.1 0.1
8 2 0.1 0.1
9 4 0.1 0.1
Less_than_10 2 0.1 0.1
“NA” 2297 64.6 64.6
Total 3557 100.0 100.0
  f7a <- as.factor(d[,"f7a"])
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5",
                     Scantron_Error="*")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31","Scantron_Error"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 569 16.0 35.7 16.0 35.7
Six_to_ten 492 13.8 30.8 29.8 66.5
Eleven_to_twenty 387 10.9 24.3 40.7 90.8
Twentyone_to_Thirty 102 2.9 6.4 43.6 97.2
Older_31 44 1.2 2.8 44.8 99.9
Scantron_Error 1 0.0 0.1 44.8 100.0
NA 1962 55.2 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0
  f7b <- as.factor(d[,"f7b"])
  levels(f7b) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 362 10.2 22.2
Yes 1262 35.5 77.6
Scantron_Error 3 0.1 0.2
NA 1930 54.3 NA
Total 3557 100.0 100.0
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
1 2 0.1 0.1
12 1 0.0 0.0
15 2 0.1 0.1
16 4 0.1 0.1
17 6 0.2 0.2
18 4 0.1 0.1
19 9 0.3 0.3
20 24 0.7 0.7
21 13 0.4 0.4
22 17 0.5 0.5
23 25 0.7 0.7
24 13 0.4 0.4
25 36 1.0 1.0
26 15 0.4 0.4
27 22 0.6 0.6
28 30 0.8 0.8
29 17 0.5 0.5
30 70 2.0 2.0
31 18 0.5 0.5
32 17 0.5 0.5
33 15 0.4 0.4
34 16 0.4 0.4
35 63 1.8 1.8
36 15 0.4 0.4
37 11 0.3 0.3
38 24 0.7 0.7
39 12 0.3 0.3
4 2 0.1 0.1
40 77 2.2 2.2
41 13 0.4 0.4
42 21 0.6 0.6
43 18 0.5 0.5
44 11 0.3 0.3
45 53 1.5 1.5
46 12 0.3 0.3
47 14 0.4 0.4
48 20 0.6 0.6
49 21 0.6 0.6
50 79 2.2 2.2
51 17 0.5 0.5
52 21 0.6 0.6
53 13 0.4 0.4
54 14 0.4 0.4
55 34 1.0 1.0
56 19 0.5 0.5
57 14 0.4 0.4
58 24 0.7 0.7
59 16 0.4 0.4
6 1 0.0 0.0
60 32 0.9 0.9
61 11 0.3 0.3
62 17 0.5 0.5
63 18 0.5 0.5
64 12 0.3 0.3
65 30 0.8 0.8
66 15 0.4 0.4
67 14 0.4 0.4
68 11 0.3 0.3
69 11 0.3 0.3
7 1 0.0 0.0
70 12 0.3 0.3
71 3 0.1 0.1
72 1 0.0 0.0
73 2 0.1 0.1
74 1 0.0 0.0
75 1 0.0 0.0
76 2 0.1 0.1
78 1 0.0 0.0
8 2 0.1 0.1
9 1 0.0 0.0
“NA” 2344 65.9 65.9
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 166 51.7 52.5 51.7 52.5
Yes 150 46.7 47.5 98.4 100.0
Scantron_Error 0 0.0 0.0 98.4 100.0
NA 5 1.6 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
10 2 0.6 0.6
12 2 0.6 0.6
13 6 1.9 1.9
14 3 0.9 0.9
15 10 3.1 3.1
16 13 4.0 4.0
17 13 4.0 4.0
18 14 4.4 4.4
19 11 3.4 3.4
20 11 3.4 3.4
21 11 3.4 3.4
22 9 2.8 2.8
23 3 0.9 0.9
25 3 0.9 0.9
26 2 0.6 0.6
27 1 0.3 0.3
28 2 0.6 0.6
30 1 0.3 0.3
32 1 0.3 0.3
33 1 0.3 0.3
34 1 0.3 0.3
35 3 0.9 0.9
40 1 0.3 0.3
45 1 0.3 0.3
48 1 0.3 0.3
“NA” 195 60.7 60.7
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 54 16.8 36.5 16.8 36.5
Six_to_ten 47 14.6 31.8 31.5 68.2
Eleven_to_twenty 37 11.5 25.0 43.0 93.2
Twentyone_to_Thirty 8 2.5 5.4 45.5 98.6
Older_31 2 0.6 1.4 46.1 100.0
Scantron_Error 0 0.0 0.0 46.1 100.0
NA 173 53.9 NA 100.0 NA
Total 321 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 20 6.2 13.6
Yes 127 39.6 86.4
Scantron_Error 0 0.0 0.0
NA 174 54.2 NA
Total 321 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
19 1 0.3 0.3
20 3 0.9 0.9
21 1 0.3 0.3
23 1 0.3 0.3
25 5 1.6 1.6
26 4 1.2 1.2
27 3 0.9 0.9
28 3 0.9 0.9
29 1 0.3 0.3
30 6 1.9 1.9
31 2 0.6 0.6
32 2 0.6 0.6
33 1 0.3 0.3
34 4 1.2 1.2
35 5 1.6 1.6
36 1 0.3 0.3
37 1 0.3 0.3
38 2 0.6 0.6
39 2 0.6 0.6
40 8 2.5 2.5
42 4 1.2 1.2
43 3 0.9 0.9
44 2 0.6 0.6
45 8 2.5 2.5
47 1 0.3 0.3
48 2 0.6 0.6
49 1 0.3 0.3
50 8 2.5 2.5
51 2 0.6 0.6
52 8 2.5 2.5
53 2 0.6 0.6
54 2 0.6 0.6
55 2 0.6 0.6
56 2 0.6 0.6
57 4 1.2 1.2
58 4 1.2 1.2
59 2 0.6 0.6
6 1 0.3 0.3
60 3 0.9 0.9
62 2 0.6 0.6
63 2 0.6 0.6
64 3 0.9 0.9
65 2 0.6 0.6
66 2 0.6 0.6
67 1 0.3 0.3
70 2 0.6 0.6
“NA” 190 59.2 59.2
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 102 48.6 51.3 48.6 51.3
Yes 97 46.2 48.7 94.8 100.0
Scantron_Error 0 0.0 0.0 94.8 100.0
NA 11 5.2 NA 100.0 NA
Total 210 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7ageE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
X0L X0L.1 X0L.2 val%
0 0 0 NA
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 39 18.6 39 18.6 39
Six_to_ten 28 13.3 28 31.9 67
Eleven_to_twenty 23 11.0 23 42.9 90
Twentyone_to_Thirty 9 4.3 9 47.1 99
Older_31 1 0.5 1 47.6 100
Scantron_Error 0 0.0 0 47.6 100
NA 110 52.4 NA 100.0 NA
Total 210 100.0 100 100.0 100
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 12 5.7 12.5
Yes 84 40.0 87.5
Scantron_Error 0 0.0 0.0
NA 114 54.3 NA
Total 210 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
16 1 0.5 0.5
20 3 1.4 1.4
21 2 1.0 1.0
24 3 1.4 1.4
25 3 1.4 1.4
26 1 0.5 0.5
27 3 1.4 1.4
28 4 1.9 1.9
30 5 2.4 2.4
31 3 1.4 1.4
32 2 1.0 1.0
33 1 0.5 0.5
35 4 1.9 1.9
36 3 1.4 1.4
37 2 1.0 1.0
38 4 1.9 1.9
39 2 1.0 1.0
40 5 2.4 2.4
42 2 1.0 1.0
43 2 1.0 1.0
45 5 2.4 2.4
46 1 0.5 0.5
47 1 0.5 0.5
49 2 1.0 1.0
50 3 1.4 1.4
51 1 0.5 0.5
52 3 1.4 1.4
56 1 0.5 0.5
58 1 0.5 0.5
59 2 1.0 1.0
60 1 0.5 0.5
63 2 1.0 1.0
65 2 1.0 1.0
66 3 1.4 1.4
70 1 0.5 0.5
“NA” 126 60.0 60.0
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 173 54.9 57.5 54.9 57.5
Yes 128 40.6 42.5 95.6 100.0
Scantron_Error 0 0.0 0.0 95.6 100.0
NA 14 4.4 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
11 1 0.3 0.3
12 1 0.3 0.3
13 1 0.3 0.3
14 4 1.3 1.3
15 8 2.5 2.5
16 9 2.9 2.9
17 8 2.5 2.5
18 15 4.8 4.8
19 12 3.8 3.8
20 9 2.9 2.9
21 6 1.9 1.9
22 6 1.9 1.9
23 3 1.0 1.0
25 6 1.9 1.9
27 1 0.3 0.3
28 3 1.0 1.0
30 2 0.6 0.6
32 1 0.3 0.3
34 1 0.3 0.3
35 2 0.6 0.6
40 1 0.3 0.3
“NA” 215 68.3 68.3
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 47 14.9 37.3 14.9 37.3
Six_to_ten 42 13.3 33.3 28.3 70.6
Eleven_to_twenty 25 7.9 19.8 36.2 90.5
Twentyone_to_Thirty 9 2.9 7.1 39.0 97.6
Older_31 3 1.0 2.4 40.0 100.0
Scantron_Error 0 0.0 0.0 40.0 100.0
NA 189 60.0 NA 100.0 NA
Total 315 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 27 8.6 20.3
Yes 106 33.7 79.7
Scantron_Error 0 0.0 0.0
NA 182 57.8 NA
Total 315 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
12 1 0.3 0.3
16 2 0.6 0.6
17 1 0.3 0.3
20 2 0.6 0.6
21 2 0.6 0.6
22 1 0.3 0.3
23 3 1.0 1.0
24 2 0.6 0.6
25 3 1.0 1.0
27 2 0.6 0.6
28 3 1.0 1.0
29 1 0.3 0.3
30 5 1.6 1.6
31 2 0.6 0.6
32 1 0.3 0.3
33 2 0.6 0.6
34 4 1.3 1.3
35 4 1.3 1.3
38 1 0.3 0.3
39 1 0.3 0.3
40 4 1.3 1.3
41 1 0.3 0.3
42 3 1.0 1.0
44 2 0.6 0.6
45 4 1.3 1.3
46 1 0.3 0.3
47 3 1.0 1.0
48 3 1.0 1.0
49 1 0.3 0.3
50 8 2.5 2.5
51 1 0.3 0.3
52 1 0.3 0.3
53 3 1.0 1.0
54 3 1.0 1.0
55 3 1.0 1.0
56 1 0.3 0.3
57 2 0.6 0.6
58 2 0.6 0.6
59 1 0.3 0.3
60 2 0.6 0.6
61 2 0.6 0.6
64 4 1.3 1.3
65 3 1.0 1.0
67 1 0.3 0.3
72 1 0.3 0.3
“NA” 212 67.3 67.3
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 175 49.2 52.2 49.2 52.2
Yes 160 44.9 47.8 94.1 100.0
Scantron_Error 0 0.0 0.0 94.1 100.0
NA 21 5.9 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
1 1 0.3 0.3
10 1 0.3 0.3
12 5 1.4 1.4
13 3 0.8 0.8
14 8 2.2 2.2
15 12 3.4 3.4
16 15 4.2 4.2
17 11 3.1 3.1
18 19 5.3 5.3
19 10 2.8 2.8
20 9 2.5 2.5
21 3 0.8 0.8
22 2 0.6 0.6
23 3 0.8 0.8
24 1 0.3 0.3
25 6 1.7 1.7
26 1 0.3 0.3
27 1 0.3 0.3
30 2 0.6 0.6
31 1 0.3 0.3
32 1 0.3 0.3
33 1 0.3 0.3
35 1 0.3 0.3
4 1 0.3 0.3
54 1 0.3 0.3
7 1 0.3 0.3
8 1 0.3 0.3
9 1 0.3 0.3
“NA” 234 65.7 65.7
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 53 14.9 31.9 14.9 31.9
Six_to_ten 45 12.6 27.1 27.5 59.0
Eleven_to_twenty 51 14.3 30.7 41.9 89.8
Twentyone_to_Thirty 14 3.9 8.4 45.8 98.2
Older_31 3 0.8 1.8 46.6 100.0
Scantron_Error 0 0.0 0.0 46.6 100.0
NA 190 53.4 NA 100.0 NA
Total 356 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 50 14.0 30.5
Yes 113 31.7 68.9
Scantron_Error 1 0.3 0.6
NA 192 53.9 NA
Total 356 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
1 1 0.3 0.3
17 1 0.3 0.3
18 1 0.3 0.3
22 1 0.3 0.3
23 2 0.6 0.6
24 1 0.3 0.3
25 4 1.1 1.1
26 2 0.6 0.6
27 2 0.6 0.6
28 3 0.8 0.8
30 10 2.8 2.8
32 4 1.1 1.1
34 1 0.3 0.3
35 5 1.4 1.4
36 1 0.3 0.3
37 1 0.3 0.3
38 3 0.8 0.8
39 1 0.3 0.3
40 4 1.1 1.1
41 3 0.8 0.8
43 3 0.8 0.8
44 1 0.3 0.3
45 5 1.4 1.4
46 2 0.6 0.6
49 1 0.3 0.3
50 8 2.2 2.2
52 1 0.3 0.3
53 2 0.6 0.6
55 8 2.2 2.2
56 4 1.1 1.1
57 2 0.6 0.6
58 2 0.6 0.6
59 1 0.3 0.3
60 2 0.6 0.6
61 2 0.6 0.6
62 3 0.8 0.8
63 2 0.6 0.6
65 2 0.6 0.6
66 1 0.3 0.3
68 2 0.6 0.6
69 2 0.6 0.6
71 1 0.3 0.3
78 1 0.3 0.3
9 1 0.3 0.3
“NA” 246 69.1 69.1
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 260 44.4 47.3 44.4 47.3
Yes 290 49.6 52.7 94.0 100.0
Scantron_Error 0 0.0 0.0 94.0 100.0
NA 35 6.0 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 1 0.2 0.2
10 3 0.5 0.5
12 4 0.7 0.7
13 6 1.0 1.0
14 9 1.5 1.5
15 22 3.8 3.8
16 25 4.3 4.3
17 30 5.1 5.1
18 38 6.5 6.5
19 16 2.7 2.7
20 16 2.7 2.7
21 11 1.9 1.9
22 8 1.4 1.4
23 1 0.2 0.2
24 2 0.3 0.3
25 6 1.0 1.0
26 1 0.2 0.2
27 1 0.2 0.2
28 7 1.2 1.2
3 1 0.2 0.2
30 6 1.0 1.0
31 1 0.2 0.2
32 2 0.3 0.3
34 1 0.2 0.2
35 3 0.5 0.5
40 1 0.2 0.2
56 1 0.2 0.2
62 1 0.2 0.2
9 2 0.3 0.3
“NA” 359 61.4 61.4
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 86 14.7 29.1 14.7 29.1
Six_to_ten 97 16.6 32.8 31.3 61.8
Eleven_to_twenty 84 14.4 28.4 45.6 90.2
Twentyone_to_Thirty 16 2.7 5.4 48.4 95.6
Older_31 12 2.1 4.1 50.4 99.7
Scantron_Error 1 0.2 0.3 50.6 100.0
NA 289 49.4 NA 100.0 NA
Total 585 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 84 14.4 28.3
Yes 211 36.1 71.0
Scantron_Error 2 0.3 0.7
NA 288 49.2 NA
Total 585 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
17 1 0.2 0.2
19 2 0.3 0.3
20 5 0.9 0.9
21 2 0.3 0.3
22 3 0.5 0.5
23 1 0.2 0.2
25 6 1.0 1.0
26 1 0.2 0.2
27 3 0.5 0.5
28 2 0.3 0.3
29 1 0.2 0.2
30 20 3.4 3.4
31 1 0.2 0.2
32 1 0.2 0.2
33 1 0.2 0.2
34 1 0.2 0.2
35 10 1.7 1.7
36 2 0.3 0.3
38 4 0.7 0.7
39 2 0.3 0.3
4 1 0.2 0.2
40 17 2.9 2.9
41 2 0.3 0.3
42 2 0.3 0.3
43 2 0.3 0.3
44 4 0.7 0.7
45 9 1.5 1.5
46 1 0.2 0.2
47 4 0.7 0.7
48 3 0.5 0.5
49 3 0.5 0.5
50 12 2.1 2.1
51 2 0.3 0.3
54 3 0.5 0.5
55 12 2.1 2.1
56 2 0.3 0.3
57 1 0.2 0.2
58 7 1.2 1.2
60 4 0.7 0.7
61 2 0.3 0.3
62 4 0.7 0.7
63 4 0.7 0.7
64 2 0.3 0.3
65 6 1.0 1.0
66 1 0.2 0.2
67 1 0.2 0.2
68 2 0.3 0.3
69 6 1.0 1.0
7 1 0.2 0.2
70 3 0.5 0.5
71 1 0.2 0.2
75 1 0.2 0.2
8 1 0.2 0.2
“NA” 390 66.7 66.7
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 920 52.5 55.6 52.5 55.6
Yes 736 42.0 44.4 94.4 100.0
Scantron_Error 0 0.0 0.0 94.4 100.0
NA 98 5.6 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 3 0.2 0.2
1 1 0.1 0.1
10 6 0.3 0.3
11 5 0.3 0.3
12 11 0.6 0.6
13 20 1.1 1.1
14 21 1.2 1.2
15 58 3.3 3.3
16 56 3.2 3.2
17 64 3.6 3.6
18 104 5.9 5.9
19 46 2.6 2.6
20 58 3.3 3.3
21 25 1.4 1.4
22 17 1.0 1.0
23 15 0.9 0.9
24 8 0.5 0.5
25 19 1.1 1.1
26 3 0.2 0.2
27 9 0.5 0.5
28 2 0.1 0.1
29 2 0.1 0.1
30 18 1.0 1.0
32 1 0.1 0.1
33 1 0.1 0.1
35 8 0.5 0.5
38 2 0.1 0.1
40 3 0.2 0.2
41 1 0.1 0.1
42 1 0.1 0.1
44 1 0.1 0.1
45 1 0.1 0.1
49 3 0.2 0.2
5 1 0.1 0.1
50 3 0.2 0.2
54 1 0.1 0.1
55 1 0.1 0.1
60 1 0.1 0.1
7 1 0.1 0.1
8 1 0.1 0.1
9 1 0.1 0.1
Less_than_10 2 0.1 0.1
“NA” 1149 65.5 65.5
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 284 16.2 37.7 16.2 37.7
Six_to_ten 233 13.3 30.9 29.5 68.7
Eleven_to_twenty 167 9.5 22.2 39.0 90.8
Twentyone_to_Thirty 46 2.6 6.1 41.6 96.9
Older_31 23 1.3 3.1 42.9 100.0
Scantron_Error 0 0.0 0.0 42.9 100.0
NA 1001 57.1 NA 100.0 NA
Total 1754 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 168 9.6 21.4
Yes 616 35.1 78.6
Scantron_Error 0 0.0 0.0
NA 970 55.3 NA
Total 1754 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
1 1 0.1 0.1
15 2 0.1 0.1
16 1 0.1 0.1
17 3 0.2 0.2
18 3 0.2 0.2
19 6 0.3 0.3
20 11 0.6 0.6
21 6 0.3 0.3
22 12 0.7 0.7
23 18 1.0 1.0
24 6 0.3 0.3
25 15 0.9 0.9
26 7 0.4 0.4
27 9 0.5 0.5
28 15 0.9 0.9
29 14 0.8 0.8
30 24 1.4 1.4
31 10 0.6 0.6
32 7 0.4 0.4
33 10 0.6 0.6
34 6 0.3 0.3
35 35 2.0 2.0
36 8 0.5 0.5
37 7 0.4 0.4
38 10 0.6 0.6
39 4 0.2 0.2
4 1 0.1 0.1
40 38 2.2 2.2
41 7 0.4 0.4
42 9 0.5 0.5
43 8 0.5 0.5
44 2 0.1 0.1
45 22 1.3 1.3
46 7 0.4 0.4
47 5 0.3 0.3
48 12 0.7 0.7
49 13 0.7 0.7
50 40 2.3 2.3
51 11 0.6 0.6
52 8 0.5 0.5
53 6 0.3 0.3
54 6 0.3 0.3
55 9 0.5 0.5
56 9 0.5 0.5
57 5 0.3 0.3
58 8 0.5 0.5
59 10 0.6 0.6
60 19 1.1 1.1
61 5 0.3 0.3
62 8 0.5 0.5
63 8 0.5 0.5
64 3 0.2 0.2
65 15 0.9 0.9
66 8 0.5 0.5
67 11 0.6 0.6
68 7 0.4 0.4
69 3 0.2 0.2
70 6 0.3 0.3
71 1 0.1 0.1
73 2 0.1 0.1
74 1 0.1 0.1
76 2 0.1 0.1
8 1 0.1 0.1
“NA” 1168 66.6 66.6
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 10 62.5 71.4 62.5 71.4
Yes 4 25.0 28.6 87.5 100.0
Scantron_Error 0 0.0 0.0 87.5 100.0
NA 2 12.5 NA 100.0 NA
Total 16 100.0 100.0 100.0 100.0
  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
16 1 6.2 6.2
19 1 6.2 6.2
21 1 6.2 6.2
23 1 6.2 6.2
“NA” 12 75.0 75.0
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 6 37.5 100 37.5 100
Six_to_ten 0 0.0 0 37.5 100
Eleven_to_twenty 0 0.0 0 37.5 100
Twentyone_to_Thirty 0 0.0 0 37.5 100
Older_31 0 0.0 0 37.5 100
Scantron_Error 0 0.0 0 37.5 100
NA 10 62.5 NA 100.0 NA
Total 16 100.0 100 100.0 100
  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 1 6.2 16.7
Yes 5 31.2 83.3
Scantron_Error 0 0.0 0.0
NA 10 62.5 NA
Total 16 100.0 100.0
  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
24 1 6.2 6.2
40 1 6.2 6.2
42 1 6.2 6.2
60 1 6.2 6.2
“NA” 12 75.0 75.0
Total 16 100.0 100.0
rm(temp.dd) 

G1

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married

All data

  g1 <- as.factor(d[,"g1"])
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5",
                     Scantron_Error="*")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married","Scantron_Error"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 2239 62.9 64.4 62.9 64.4
Separated 123 3.5 3.5 66.4 68.0
Divorced 571 16.1 16.4 82.5 84.4
Widowed 209 5.9 6.0 88.3 90.4
Never_Married 332 9.3 9.6 97.7 99.9
Scantron_Error 2 0.1 0.1 97.7 100.0
NA 81 2.3 NA 100.0 NA
Total 3557 100.0 100.0 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 187 58.3 58.8
Separated 9 2.8 2.8
Divorced 66 20.6 20.8
Widowed 13 4.0 4.1
Never_Married 43 13.4 13.5
Scantron_Error 0 0.0 0.0
NA 3 0.9 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 132 62.9 63.8
Separated 5 2.4 2.4
Divorced 32 15.2 15.5
Widowed 10 4.8 4.8
Never_Married 28 13.3 13.5
Scantron_Error 0 0.0 0.0
NA 3 1.4 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 219 69.5 71.1
Separated 7 2.2 2.3
Divorced 47 14.9 15.3
Widowed 11 3.5 3.6
Never_Married 24 7.6 7.8
Scantron_Error 0 0.0 0.0
NA 7 2.2 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 175 49.2 50.4
Separated 15 4.2 4.3
Divorced 69 19.4 19.9
Widowed 23 6.5 6.6
Never_Married 65 18.3 18.7
Scantron_Error 0 0.0 0.0
NA 9 2.5 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 350 59.8 60.8
Separated 32 5.5 5.6
Divorced 110 18.8 19.1
Widowed 42 7.2 7.3
Never_Married 42 7.2 7.3
Scantron_Error 0 0.0 0.0
NA 9 1.5 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 1169 66.6 68.6
Separated 55 3.1 3.2
Divorced 241 13.7 14.1
Widowed 109 6.2 6.4
Never_Married 129 7.4 7.6
Scantron_Error 2 0.1 0.1
NA 49 2.8 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val%
Married_partner 7 43.8 46.7
Separated 0 0.0 0.0
Divorced 6 37.5 40.0
Widowed 1 6.2 6.7
Never_Married 1 6.2 6.7
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

G2

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets

All data

  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 765 21.5 100
Scantron_Error 0 0.0 0
NA 2792 78.5 NA
Total 3557 100.0 100
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 2309 64.9 100
Scantron_Error 0 0.0 0
NA 1248 35.1 NA
Total 3557 100.0 100
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 487 13.7 100
Scantron_Error 0 0.0 0
NA 3070 86.3 NA
Total 3557 100.0 100
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 78 2.2 100
Scantron_Error 0 0.0 0
NA 3479 97.8 NA
Total 3557 100.0 100
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 199 5.6 100
Scantron_Error 0 0.0 0
NA 3358 94.4 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 83 25.9 100
Scantron_Error 0 0.0 0
NA 238 74.1 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 193 60.1 100
Scantron_Error 0 0.0 0
NA 128 39.9 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 64 19.9 100
Scantron_Error 0 0.0 0
NA 257 80.1 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 8 2.5 100
Scantron_Error 0 0.0 0
NA 313 97.5 NA
Total 321 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 22 6.9 100
Scantron_Error 0 0.0 0
NA 299 93.1 NA
Total 321 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 41 19.5 100
Scantron_Error 0 0.0 0
NA 169 80.5 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 134 63.8 100
Scantron_Error 0 0.0 0
NA 76 36.2 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 44 21 100
Scantron_Error 0 0 0
NA 166 79 NA
Total 210 100 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 9 4.3 100
Scantron_Error 0 0.0 0
NA 201 95.7 NA
Total 210 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 17 8.1 100
Scantron_Error 0 0.0 0
NA 193 91.9 NA
Total 210 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 43 13.7 100
Scantron_Error 0 0.0 0
NA 272 86.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 227 72.1 100
Scantron_Error 0 0.0 0
NA 88 27.9 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 43 13.7 100
Scantron_Error 0 0.0 0
NA 272 86.3 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 13 4.1 100
Scantron_Error 0 0.0 0
NA 302 95.9 NA
Total 315 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 27 8.6 100
Scantron_Error 0 0.0 0
NA 288 91.4 NA
Total 315 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 99 27.8 100
Scantron_Error 0 0.0 0
NA 257 72.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 195 54.8 100
Scantron_Error 0 0.0 0
NA 161 45.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 56 15.7 100
Scantron_Error 0 0.0 0
NA 300 84.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 13 3.7 100
Scantron_Error 0 0.0 0
NA 343 96.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 23 6.5 100
Scantron_Error 0 0.0 0
NA 333 93.5 NA
Total 356 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 99 27.8 100
Scantron_Error 0 0.0 0
NA 257 72.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 195 54.8 100
Scantron_Error 0 0.0 0
NA 161 45.2 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 56 15.7 100
Scantron_Error 0 0.0 0
NA 300 84.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 13 3.7 100
Scantron_Error 0 0.0 0
NA 343 96.3 NA
Total 356 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 23 6.5 100
Scantron_Error 0 0.0 0
NA 333 93.5 NA
Total 356 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 335 19.1 100
Scantron_Error 0 0.0 0
NA 1419 80.9 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 1188 67.7 100
Scantron_Error 0 0.0 0
NA 566 32.3 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 220 12.5 100
Scantron_Error 0 0.0 0
NA 1534 87.5 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 32 1.8 100
Scantron_Error 0 0.0 0
NA 1722 98.2 NA
Total 1754 100.0 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 84 4.8 100
Scantron_Error 0 0.0 0
NA 1670 95.2 NA
Total 1754 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 7 43.8 100
Scantron_Error 0 0.0 0
NA 9 56.2 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 8 50 100
Scantron_Error 0 0 0
NA 8 50 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 1 6.2 100
Scantron_Error 0 0.0 0
NA 15 93.8 NA
Total 16 100.0 100
  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 0 0 NaN
Scantron_Error 0 0 NaN
NA 16 100 NA
Total 16 100 100
rm(temp.dd) 

G3

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer

All data

  g3 <- as.factor(d[,"g3"])
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99",
                       Scantron_Error="*")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer","Scantron_Error"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 3257 91.6 95.2
Bisexual 22 0.6 0.6
homosexual 54 1.5 1.6
Other 22 0.6 0.6
Prefer_not_to_answer 66 1.9 1.9
Scantron_Error 1 0.0 0.0
NA 135 3.8 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 302 94.1 94.7
Bisexual 2 0.6 0.6
homosexual 8 2.5 2.5
Other 1 0.3 0.3
Prefer_not_to_answer 5 1.6 1.6
Scantron_Error 1 0.3 0.3
NA 2 0.6 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 189 90.0 91.7
Bisexual 2 1.0 1.0
homosexual 11 5.2 5.3
Other 1 0.5 0.5
Prefer_not_to_answer 3 1.4 1.5
Scantron_Error 0 0.0 0.0
NA 4 1.9 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 289 91.7 95.1
Bisexual 5 1.6 1.6
homosexual 6 1.9 2.0
Other 0 0.0 0.0
Prefer_not_to_answer 4 1.3 1.3
Scantron_Error 0 0.0 0.0
NA 11 3.5 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 327 91.9 96.5
Bisexual 1 0.3 0.3
homosexual 5 1.4 1.5
Other 0 0.0 0.0
Prefer_not_to_answer 6 1.7 1.8
Scantron_Error 0 0.0 0.0
NA 17 4.8 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 534 91.3 95.2
Bisexual 3 0.5 0.5
homosexual 5 0.9 0.9
Other 8 1.4 1.4
Prefer_not_to_answer 11 1.9 2.0
Scantron_Error 0 0.0 0.0
NA 24 4.1 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 1603 91.4 95.5
Bisexual 8 0.5 0.5
homosexual 18 1.0 1.1
Other 12 0.7 0.7
Prefer_not_to_answer 37 2.1 2.2
Scantron_Error 0 0.0 0.0
NA 76 4.3 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 13 81.2 86.7
Bisexual 1 6.2 6.7
homosexual 1 6.2 6.7
Other 0 0.0 0.0
Prefer_not_to_answer 0 0.0 0.0
Scantron_Error 0 0.0 0.0
NA 1 6.2 NA
Total 16 100.0 100.0
rm(temp.dd) 

G4

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know

All data

  g4a <- as.factor(d[,"g4a"])
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Scantron_Error="*")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Scantron_Error"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  g4b <- as.factor(d[,"g4b"])
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88",
                      Scantron_Error="*")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know","Scantron_Error"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  g4c <- as.factor(d[,"g4c"])
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88",
                      Scantron_Error="*")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know","Scantron_Error"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 100 2.8 3.1
Some_high_school 259 7.3 8.1
High_school_graduate_GED 793 22.3 24.7
Vocational_school 116 3.3 3.6
Some_college 704 19.8 21.9
Associate_degree 278 7.8 8.7
College_graduate 426 12.0 13.3
Some_graduate_education 96 2.7 3.0
Graduate_degree 441 12.4 13.7
Scantron_Error 0 0.0 0.0
NA 344 9.7 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 849 23.9 27.2
Some_high_school 446 12.5 14.3
High_school_graduate_GED 652 18.3 20.9
Vocational_school 88 2.5 2.8
Some_college 111 3.1 3.5
Associate_degree 51 1.4 1.6
College_graduate 103 2.9 3.3
Some_graduate_education 18 0.5 0.6
Graduate_degree 81 2.3 2.6
Dont_know 728 20.5 23.3
Scantron_Error 0 0.0 0.0
NA 430 12.1 NA
Total 3557 100.0 100.0
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 597 16.8 19.0
Some_high_school 500 14.1 15.9
High_school_graduate_GED 893 25.1 28.4
Vocational_school 120 3.4 3.8
Some_college 164 4.6 5.2
Associate_degree 100 2.8 3.2
College_graduate 156 4.4 5.0
Some_graduate_education 22 0.6 0.7
Graduate_degree 99 2.8 3.2
Dont_know 490 13.8 15.6
Scantron_Error 0 0.0 0.0
NA 416 11.7 NA
Total 3557 100.0 100.0
rm(temp.dd) 

G5

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other

All data

  g5 <- as.factor(d[,"g5"])
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8",
                     Scantron_Error="*")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other","Scantron_Error"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 880 24.7 25.7
part_time 197 5.5 5.7
unemployed 58 1.6 1.7
Retired 1629 45.8 47.5
disability_permanently 456 12.8 13.3
disability_for_a_time 36 1.0 1.1
Volunteer_work 13 0.4 0.4
Other 69 1.9 2.0
Scantron_Error 90 2.5 2.6
NA 129 3.6 NA
Total 3557 100.0 100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 89 27.7 28.3
part_time 25 7.8 7.9
unemployed 9 2.8 2.9
Retired 145 45.2 46.0
disability_permanently 30 9.3 9.5
disability_for_a_time 5 1.6 1.6
Volunteer_work 0 0.0 0.0
Other 7 2.2 2.2
Scantron_Error 5 1.6 1.6
NA 6 1.9 NA
Total 321 100.0 100.0
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 72 34.3 35.0
part_time 11 5.2 5.3
unemployed 1 0.5 0.5
Retired 91 43.3 44.2
disability_permanently 20 9.5 9.7
disability_for_a_time 2 1.0 1.0
Volunteer_work 1 0.5 0.5
Other 3 1.4 1.5
Scantron_Error 5 2.4 2.4
NA 4 1.9 NA
Total 210 100.0 100.0
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 94 29.8 30.9
part_time 18 5.7 5.9
unemployed 7 2.2 2.3
Retired 140 44.4 46.1
disability_permanently 24 7.6 7.9
disability_for_a_time 4 1.3 1.3
Volunteer_work 2 0.6 0.7
Other 11 3.5 3.6
Scantron_Error 4 1.3 1.3
NA 11 3.5 NA
Total 315 100.0 100.0
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 49 13.8 14.4
part_time 18 5.1 5.3
unemployed 8 2.2 2.3
Retired 167 46.9 49.0
disability_permanently 75 21.1 22.0
disability_for_a_time 5 1.4 1.5
Volunteer_work 2 0.6 0.6
Other 11 3.1 3.2
Scantron_Error 6 1.7 1.8
NA 15 4.2 NA
Total 356 100.0 100.0
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 130 22.2 23.0
part_time 30 5.1 5.3
unemployed 9 1.5 1.6
Retired 278 47.5 49.1
disability_permanently 86 14.7 15.2
disability_for_a_time 4 0.7 0.7
Volunteer_work 3 0.5 0.5
Other 9 1.5 1.6
Scantron_Error 17 2.9 3.0
NA 19 3.2 NA
Total 585 100.0 100.0
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 441 25.1 26.2
part_time 94 5.4 5.6
unemployed 24 1.4 1.4
Retired 802 45.7 47.7
disability_permanently 219 12.5 13.0
disability_for_a_time 15 0.9 0.9
Volunteer_work 5 0.3 0.3
Other 28 1.6 1.7
Scantron_Error 52 3.0 3.1
NA 74 4.2 NA
Total 1754 100.0 100.0
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 5 31.2 31.2
part_time 1 6.2 6.2
unemployed 0 0.0 0.0
Retired 6 37.5 37.5
disability_permanently 2 12.5 12.5
disability_for_a_time 1 6.2 6.2
Volunteer_work 0 0.0 0.0
Other 0 0.0 0.0
Scantron_Error 1 6.2 6.2
Total 16 100.0 100.0
rm(temp.dd) 

G6

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance

All data

  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd) 

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 1202 33.8 100
Scantron_Error 0 0.0 0
NA 2355 66.2 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 371 10.4 100
Scantron_Error 0 0.0 0
NA 3186 89.6 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")
Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 232 6.5 100
Scantron_Error 0 0.0 0
NA 3325 93.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 105 3 100
Scantron_Error 0 0 0
NA 3452 97 NA
Total 3557 100 100
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")
Medicaid or other state provided insurance
n % val%
Medicaid_state 510 14.3 100
Scantron_Error 0 0.0 0
NA 3047 85.7 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")
Medicare/government insurance
n % val%
Medicare_government 1620 45.5 100
Scantron_Error 0 0.0 0
NA 1937 54.5 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")
VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 601 16.9 100
Scantron_Error 0 0.0 0
NA 2956 83.1 NA
Total 3557 100.0 100
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")
I do not have any medical insurance
n % val%
Do_not_have 63 1.8 100
Scantron_Error 0 0.0 0
NA 3494 98.2 NA
Total 3557 100.0 100
rm(temp.dd)